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Taghi M. Khoshgoftaar
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2020 – today
- 2026
[j232]Misbah Khan
, Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Artificial Intelligence and Machine Learning for Job Hazard Analysis: A Survey. IEEE Access 14: 25605-25622 (2026)
[j231]Richard A. Bauder
, Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Characterizing and Harmonizing Five Public Workplace Injury Datasets for Research and Modeling. IEEE Access 14: 73765-73791 (2026)
[j230]Andy Sinclair
, Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Assessing Skip-Tracing Information in Repossession Prediction Using Ensemble Learning and SHAP Analysis. IEEE Access 14: 104341-104354 (2026)
[j229]Misbah Khan
, Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Hallucination Is Not One Thing: A Two-Axis Taxonomy for Structured Diagnosis in Generative AI. IEEE Access 14: 119273-119304 (2026)
[j228]Andy Sinclair
, Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Predictive Gains and Governance Tradeoffs in Socioeconomic Automobile Repossession Modeling. IEEE Access 14: 141733-141752 (2026)
[j227]Mohammad Sadegh Khorshidi, José M. Merigó
, Ghassan Beydoun
, Borko Furht, Taghi M. Khoshgoftaar:
A bibliometric analysis of the first decade of the Journal of Big Data. J. Big Data 13(1): 80 (2026)
[j226]Andy Sinclair, Preston Billion-Polak, Taghi M. Khoshgoftaar:
Predictive modeling for auto repossession success: a comparative evaluation. J. Big Data 13(1): 107 (2026)
[j225]Chelsea M. Zuvieta, Gonzalo A. Vivian, Taghi M. Khoshgoftaar:
Order-based frameworks for predicting and classifying absence duration following an occupational injury. J. Big Data 13(1): 117 (2026)
[c430]Itay B. Shabat, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar:
Machine Learning Models for Predicting Cardiac Mortality in Chronic Heart Failure Patients. IRI 2026: 207-212
[c429]Ethan D. Curtis, Preston Billion-Polak, Taghi M. Khoshgoftaar:
Stochastic $k$-Simplex Embedding in High-Dimensional Mass-Spectrometry Cancer Classification. IRI 2026: 213-219
[c428]Preston Billion-Polak, Taghi M. Khoshgoftaar:
Comparing Encoding Architectures for Supervised Contrastive Learning in Tabular Classification. IRI 2026: 269-274
[c427]Andy Sinclair, Preston Billion-Polak, Taghi M. Khoshgoftaar:
A CatBoost-Based Ablation Study of Scheduling Heuristics in Automobile Repossession. IRI 2026: 298-303
[c426]Chelsea M. Zuvieta, Gonzalo A. Vivian, Taghi M. Khoshgoftaar:
Risk-Stratified Explainability of Attorney Involvement in Workers' Compensation Claims. IRI 2026: 304-309
[c425]Romina Gotzmann
, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar:
Comparing Clinical and Machine Learning Approaches for Cardiac Mortality Risk Prediction. IRI 2026: 383-388
[c424]Yifan Yu, Preston Billion-Polak, Taghi M. Khoshgoftaar:
TRACE: Transformer for Regularized and Accurate Cortical ECoG Motor Decoding. IRI 2026: 425-431- 2025
[j224]Chelsea M. Zuvieta
, Joffrey L. Leevy
, Taghi M. Khoshgoftaar:
A Survey on Statistical and ML-Based Demand Forecasting Methods for Spare Parts in Aviation. IEEE Access 13: 44800-44816 (2025)
[j223]Mary Anne Walauskis
, Taghi M. Khoshgoftaar:
SHAP-Based Feature Selection for Enhanced Unsupervised Labeling. IEEE Access 13: 130098-130109 (2025)
[j222]John T. Hancock
, Richard A. Bauder, Taghi M. Khoshgoftaar:
SHAP as a Data Reduction Technique for Highly Imbalanced Big Data. Int. J. Artif. Intell. Tools 34(4-5): 2540001:1-2540001:27 (2025)
[j221]Joffrey L. Leevy
, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
Evaluating the Impact of Majority versus Minority Class Training for One-Class Classification. Int. J. Artif. Intell. Tools 34(4-5): 2540002:1-2540002:17 (2025)
[j220]John T. Hancock, Taghi M. Khoshgoftaar, Qianxin Liang:
A problem-agnostic approach to feature selection and analysis using SHAP. J. Big Data 12(1): 12 (2025)
[j219]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
Unsupervised label generation for severely imbalanced fraud data. J. Big Data 12(1): 63 (2025)
[j218]Preston Billion-Polak
, Taghi M. Khoshgoftaar:
Evaluating techniques from low-shot learning on traditional imbalanced classification tasks. J. Big Data 12(1): 110 (2025)
[j217]Robert K. L. Kennedy
, Flavio Villanustre, Taghi M. Khoshgoftaar:
Unsupervised feature selection and class labeling for credit card fraud. J. Big Data 12(1): 111 (2025)
[j216]Borko Furht, Taghi M. Khoshgoftaar:
Data science, big data, and machine learning are coming of age. J. Big Data 12(1): 161 (2025)
[j215]Gonzalo A. Vivian, Richard A. Bauder, Taghi M. Khoshgoftaar:
A comprehensive survey on machine learning for workplace injury analysis: risk prediction, return to work strategies, and demographic insights. J. Big Data 12(1): 167 (2025)
[j214]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
Scalable unsupervised labeling with SHAP feature selection for fraud detection in imbalanced data. J. Big Data 12(1): 236 (2025)
[j213]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
A novel approach to automating unsupervised estimation of class distribution. J. Big Data 12(1): 237 (2025)
[j212]Ethan D. Curtis, Preston Billion-Polak
, Taghi M. Khoshgoftaar, Borko Furht:
A review of distinct machine learning classifiers for healthcare fraud detection. J. Big Data 12(1): 238 (2025)
[c423]Andy Sinclair
, Preston Billion-Polak, Taghi M. Khoshgoftaar:
Unlocking Machine Learning Insights into a Novel Auto Repossession Dataset. CogMI 2025: 433-439
[c422]Qianxin Liang, Richard A. Bauder, Taghi M. Khoshgoftaar:
An Interpretable and Efficient Random Undersampling-enhanced SHAP Framework for Medicare Fraud Detection. CIC 2025: 11-20
[c421]Chelsea M. Zuvieta, Gonzalo A. Vivian, Taghi M. Khoshgoftaar:
A Comparison of Rank-Ordering and Classification for Occupational Injury Absence Duration. CIC 2025: 148-157
[c420]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
Choosing the Right Metrics: A Study of Performance Measurement for Binary Classification in Imbalanced and Big Data. FLAIRS 2025
[c419]Zahra Salekshahrezaee, Mary Anne Walauskis, Taghi M. Khoshgoftaar:
Unsupervised Feature Extraction using Convolutional Autoencoder for Credit Card Fraud Detection. ICMLA 2025: 440-445
[c418]Gonzalo A. Vivian, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar:
Return-to-Work Classification of Occupational Injury Claims via Rank-Ordering. ICMLA 2025: 484-491
[c417]Kehan Gao, Sarah Tasneem, Taghi M. Khoshgoftaar:
Medical Imaging with Deep Learning: A Comparison of CNN and Transformer Models. ICMLA 2025: 523-530
[c416]Preston Billion-Polak, Andy Sinclair, Taghi M. Khoshgoftaar:
Applying Machine Learning to Classify Automobile Repossession Success. ICMLA 2025: 563-568
[c415]Preston Billion-Polak, Taghi M. Khoshgoftaar:
Examining the Impact of Feature Selection for Contrastive Learning in Fraud Detection. ICTAI 2025: 560-566
[c414]Gonzalo A. Vivian, Chelsea M. Zuvieta, Taghi M. Khoshgoftaar:
A Novel Technique to Rank-Order Occupational Injury Claims for Return-to-Work Prediction. ICTAI 2025: 1373-1380
[c413]Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
The Impact of Class Imbalance on Unsupervised Deep Anomaly Detection for Cognitive Data. IRI 2025: 222-227
[c412]Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
Unsupervised Cognitive Impairment Detection Using Convolutional Autoencoders and Isolation Forest. IRI 2025: 295-300
[c411]John T. Hancock, Robert K. L. Kennedy
, Mary Anne Walauskis, Taghi M. Khoshgoftaar:
A New and Effective Technique for Unsupervised Labeling and Feature Selection with Applications in Healthcare Fraud Detection. IRI 2025: 301-306- 2024
[j211]Preston Billion-Polak
, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Low-shot learning and class imbalance: a survey. J. Big Data 11(1): 1 (2024)
[j210]John T. Hancock, Huanjing Wang, Taghi M. Khoshgoftaar, Qianxin Liang:
Data reduction techniques for highly imbalanced medicare Big Data. J. Big Data 11(1): 8 (2024)
[j209]Safak Kayikci
, Taghi M. Khoshgoftaar:
Blockchain meets machine learning: a survey. J. Big Data 11(1): 9 (2024)
[j208]Robert K. L. Kennedy
, Flavio Villanustre
, Taghi M. Khoshgoftaar, Zahra Salekshahrezaee:
Synthesizing class labels for highly imbalanced credit card fraud detection data. J. Big Data 11(1): 38 (2024)
[j207]Huanjing Wang
, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods. J. Big Data 11(1): 44 (2024)
[j206]Michael Lowe, Joseph D. Prusa, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Advancing machine learning with OCR2SEQ: an innovative approach to multi-modal data augmentation. J. Big Data 11(1): 86 (2024)
[c410]Joffrey L. Leevy, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
A Review of Unsupervised Anomaly Detection Techniques for Health Insurance Fraud. BigDataService 2024: 141-149
[c409]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
New Class Labeling and Evaluation Methodology for Balanced and Highly Imbalanced Data. ICMLA 2024: 264-271
[c408]Preston Billion-Polak
, Taghi M. Khoshgoftaar:
An Evaluation of Low-Shot Learning Techniques for the Detection of Credit Card Fraud. ICMLA 2024: 304-311
[c407]Preston Billion-Polak
, Taghi M. Khoshgoftaar:
A Comparison of Low-Shot Learning Methods for Imbalanced Binary Classification. ICTAI 2024: 136-143
[c406]Mary Anne Walauskis, Taghi M. Khoshgoftaar:
Confident Labels: A Novel Approach to New Class Labeling and Evaluation on Highly Imbalanced Data. ICTAI 2024: 232-239
[c405]Qianxin Liang, Richard A. Bauder, Taghi M. Khoshgoftaar:
Enhancing Medicare Fraud Detection: Random Undersampling Followed by SHAP-Driven Feature Selection with Big Data. ICTAI 2024: 256-263
[c404]Michael Lowe, Joseph D. Prusa, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Context-Relevant Denoising for Unsupervised Domain-Adapted Sentence Embeddings. IRI 2024: 13-18
[c403]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
Synthesizing Class Labels for Balanced and Highly Imbalanced Cognition Data. IRI 2024: 19-24
[c402]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
Impact of Class Imbalance on Unsupervised Label Generation for Medicare Fraud Detection. IRI 2024: 216-221- 2023
[j205]Debarshi Datta
, Safiya George Dalmida
, Laurie Martinez, David Newman
, Javad Hashemi, Taghi M. Khoshgoftaar, Connor Shorten, Candice Sareli, Paula Eckardt:
Using machine learning to identify patient characteristics to predict mortality of in-patients with COVID-19 in South Florida. Frontiers Digit. Health 5 (2023)
[j204]Justin M. Johnson
, Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
Learning from Highly Imbalanced Big Data with Label Noise. Int. J. Artif. Intell. Tools 32(5): 2360003:1-2360003:20 (2023)
[j203]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
The effect of feature extraction and data sampling on credit card fraud detection. J. Big Data 10(1): 6 (2023)
[j202]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
An approach to application-layer DoS detection. J. Big Data 10(1): 22 (2023)
[j201]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Evaluating classifier performance with highly imbalanced Big Data. J. Big Data 10(1): 42 (2023)
[j200]Joffrey L. Leevy, Justin M. Johnson, John T. Hancock, Taghi M. Khoshgoftaar:
Threshold optimization and random undersampling for imbalanced credit card data. J. Big Data 10(1): 58 (2023)
[j199]Safak Kayikci
, Taghi M. Khoshgoftaar:
Breast cancer prediction using gated attentive multimodal deep learning. J. Big Data 10(1): 62 (2023)
[j198]Robert K. L. Kennedy
, Zahra Salekshahrezaee, Flavio Villanustre
, Taghi M. Khoshgoftaar:
Iterative cleaning and learning of big highly-imbalanced fraud data using unsupervised learning. J. Big Data 10(1): 106 (2023)
[j197]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Comparative analysis of binary and one-class classification techniques for credit card fraud data. J. Big Data 10(1): 118 (2023)
[j196]John T. Hancock, Richard A. Bauder, Huanjing Wang, Taghi M. Khoshgoftaar:
Explainable machine learning models for Medicare fraud detection. J. Big Data 10(1): 154 (2023)
[j195]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
Investigating the effectiveness of one-class and binary classification for fraud detection. J. Big Data 10(1): 157 (2023)
[j194]Justin M. Johnson
, Taghi M. Khoshgoftaar:
Data-Centric AI for Healthcare Fraud Detection. SN Comput. Sci. 4(4): 389 (2023)
[j193]John T. Hancock
, Taghi M. Khoshgoftaar:
Exploring Maximum Tree Depth and Random Undersampling in Ensemble Trees to Optimize the Classification of Imbalanced Big Data. SN Comput. Sci. 4(5): 462 (2023)
[c401]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
A Novel Approach to Synthesize Class Labels in Highly Imbalanced Large Data. CogMI 2023: 11-17
[c400]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
A Comparative Study of Model-Agnostic and Importance-Based Feature Selection Approaches. CogMI 2023: 75-82
[c399]John T. Hancock, Taghi M. Khoshgoftaar:
Data Reduction to Improve the Performance of One-Class Classifiers on Highly Imbalanced Big Data. ICMLA 2023: 465-471
[c398]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Azadeh Abdollah Zadeh:
One-Class Classifier Performance: Comparing Majority versus Minority Class Training. ICTAI 2023: 86-91
[c397]John T. Hancock, Richard A. Bauder, Taghi M. Khoshgoftaar:
A Model-Agnostic Feature Selection Technique to Improve the Performance of One-Class Classifiers. ICTAI 2023: 92-98
[c396]Huanjing Wang, Qianxin Liang, John T. Hancock, Taghi M. Khoshgoftaar:
Enhancing Credit Card Fraud Detection Through a Novel Ensemble Feature Selection Technique. IRI 2023: 121-126
[c395]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar:
Assessing One-Class and Binary Classification Approaches for Identifying Medicare Fraud. IRI 2023: 267-272
[c394]Robert K. L. Kennedy
, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method. IRI 2023: 303-308
[c393]Huanjing Wang, John T. Hancock, Taghi M. Khoshgoftaar:
Improving Medicare Fraud Detection through Big Data Size Reduction Techniques. SOSE 2023: 208-217- 2022
[j192]Joffrey L. Leevy
, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
IoT information theft prediction using ensemble feature selection. J. Big Data 9(1): 6 (2022)
[j191]Rick Sauber-Cole, Taghi M. Khoshgoftaar:
The use of generative adversarial networks to alleviate class imbalance in tabular data: a survey. J. Big Data 9(1): 98 (2022)
[j190]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
A new feature popularity framework for detecting cyberattacks using popular features. J. Big Data 9(1): 119 (2022)
[j189]Justin M. Johnson
, Taghi M. Khoshgoftaar:
A Survey on Classifying Big Data with Label Noise. ACM J. Data Inf. Qual. 14(4): 23:1-23:43 (2022)
[j188]Justin M. Johnson
, Taghi M. Khoshgoftaar:
Encoding High-Dimensional Procedure Codes for Healthcare Fraud Detection. SN Comput. Sci. 3(5): 362 (2022)
[j187]John T. Hancock
, Taghi M. Khoshgoftaar:
Hyperparameter Tuning for Medicare Fraud Detection in Big Data. SN Comput. Sci. 3(6): 440 (2022)
[c392]Robert K. L. Kennedy
, Zahra Salekshahrezaee, Taghi M. Khoshgoftaar:
A Novel Approach for Unsupervised Learning of Highly-Imbalanced Data. CogMI 2022: 52-58
[c391]John T. Hancock, Justin M. Johnson, Taghi M. Khoshgoftaar:
A Comparative Approach to Threshold Optimization for Classifying Imbalanced Data. CIC 2022: 135-142
[c390]Erika Cardenas, Connor Shorten, Taghi M. Khoshgoftaar, Borivoje Furht:
A Comparison of House Price Classification with Structured and Unstructured Text Data. FLAIRS 2022
[c389]Connor Shorten, Taghi M. Khoshgoftaar:
An Exploration of Consistency Learning with Data Augmentation. FLAIRS 2022
[c388]Connor Shorten, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida
, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Predicting the Severity of COVID-19 Respiratory Illness with Deep Learning. FLAIRS 2022
[c387]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
Informative Evaluation Metrics for Highly Imbalanced Big Data Classification. ICMLA 2022: 1419-1426
[c386]Justin M. Johnson, Taghi M. Khoshgoftaar:
Cost-Sensitive Ensemble Learning for Highly Imbalanced Classification. ICMLA 2022: 1427-1434
[c385]Joffrey L. Leevy, Taghi M. Khoshgoftaar, John T. Hancock:
Evaluating Performance Metrics for Credit Card Fraud Classification. ICTAI 2022: 1336-1341
[c384]Rick Sauber-Cole, Taghi M. Khoshgoftaar, Justin M. Johnson:
GANs for Class-Imbalanced Data: A Meta-Analysis of GitHub Projects. ICTAI 2022: 1419-1424
[c383]Connor Shorten, Erika Cardenas, Taghi M. Khoshgoftaar, Javad Hashemi, Safiya George Dalmida
, David Newman, Debarshi Datta, Laurie Martinez, Candice Sareli, Paula Eckard:
Exploring Language-Interfaced Fine-Tuning for COVID-19 Patient Survival Classification. ICTAI 2022: 1449-1454
[c382]Zahra Salekshahrezaee, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Class-Imbalanced Study with Feature Extraction via PCA and Convolutional Autoencoder. IRI 2022: 63-68
[c381]Justin M. Johnson, Taghi M. Khoshgoftaar:
Healthcare Provider Summary Data for Fraud Classification. IRI 2022: 236-242
[c380]John T. Hancock, Taghi M. Khoshgoftaar:
Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection. IRI 2022: 243-249
[c379]John T. Hancock, Taghi M. Khoshgoftaar, Justin M. Johnson:
The Effects of Random Undersampling for Big Data Medicare Fraud Detection. SOSE 2022: 141-146- 2021
[j186]Joffrey L. Leevy
, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting cybersecurity attacks across different network features and learners. J. Big Data 8(1): 1-29 (2021)
[j185]Zahra Salekshahrezaee
, Joffrey L. Leevy
, Taghi M. Khoshgoftaar:
A reconstruction error-based framework for label noise detection. J. Big Data 8(1): 1-16 (2021)
[j184]Connor Shorten
, Taghi M. Khoshgoftaar, Borko Furht:
Deep Learning applications for COVID-19. J. Big Data 8(1): 1-54 (2021)
[j183]Flavio Villanustre
, Arjuna Chala, Roger Dev
, Lili Xu, Jesse Shaw, Borko Furht, Taghi M. Khoshgoftaar:
Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platformm. J. Big Data 8(1): 33 (2021)
[j182]Richard Zuech
, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting web attacks using random undersampling and ensemble learners. J. Big Data 8(1): 75 (2021)
[j181]Connor Shorten
, Taghi M. Khoshgoftaar, Borko Furht:
Text Data Augmentation for Deep Learning. J. Big Data 8(1): 101 (2021)
[j180]Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar:
A literature review on one-class classification and its potential applications in big data. J. Big Data 8(1): 122 (2021)
[j179]John T. Hancock
, Taghi M. Khoshgoftaar:
Gradient Boosted Decision Tree Algorithms for Medicare Fraud Detection. SN Comput. Sci. 2(4): 268 (2021)
[j178]Justin M. Johnson
, Taghi M. Khoshgoftaar:
Medical Provider Embeddings for Healthcare Fraud Detection. SN Comput. Sci. 2(4): 276 (2021)
[c378]Joffrey L. Leevy, Taghi M. Khoshgoftaar, Jared M. Peterson:
Mitigating Class Imbalance for IoT Network Intrusion Detection: A Survey. BigDataService 2021: 143-148
[c377]John T. Hancock, Taghi M. Khoshgoftaar:
Leveraging LightGBM for Categorical Big Data. BigDataService 2021: 149-154
[c376]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
An Examination of Neural Networks on Cluster Computers. BigDataService 2021: 155-160
[c375]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
An Easy-to-Classify Approach for the Bot-IoT Dataset. CogMI 2021: 172-179
[c374]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Naeem Seliya:
IoT Reconnaissance Attack Classification with Random Undersampling and Ensemble Feature Selection. CIC 2021: 41-49
[c373]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting SQL Injection Web Attacks Using Ensemble Learners and Data Sampling. CSR 2021: 27-34
[c372]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Feature Popularity Between Different Web Attacks with Supervised Feature Selection Rankers. ICMLA 2021: 30-37
[c371]Connor Shorten, Taghi M. Khoshgoftaar:
KerasBERT: Modeling the Keras Language. ICMLA 2021: 219-226
[c370]John T. Hancock, Taghi M. Khoshgoftaar, Joffrey L. Leevy:
Detecting SSH and FTP Brute Force Attacks in Big Data. ICMLA 2021: 760-765
[c369]Joffrey L. Leevy, John T. Hancock, Taghi M. Khoshgoftaar, Jared M. Peterson:
Detecting Information Theft Attacks in the Bot-IoT Dataset. ICMLA 2021: 807-812
[c368]Justin M. Johnson, Taghi M. Khoshgoftaar:
Robust Thresholding Strategies for Highly Imbalanced and Noisy Data. ICMLA 2021: 1182-1188
[c367]Connor Shorten, Taghi M. Khoshgoftaar:
Investigating the Generalization of Image Classifiers with Augmented Test Sets. ICTAI 2021: 10-17
[c366]Zahra Salekshahrezaee
, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
Feature Extraction for Class Imbalance Using a Convolutional Autoencoder and Data Sampling. ICTAI 2021: 217-223
[c365]Robert K. L. Kennedy
, Justin M. Johnson, Taghi M. Khoshgoftaar:
The Effects of Class Label Noise on Highly-Imbalanced Big Data. ICTAI 2021: 1427-1433
[c364]Justin M. Johnson, Taghi M. Khoshgoftaar:
Output Thresholding for Ensemble Learners and Imbalanced Big Data. ICTAI 2021: 1449-1454
[c363]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detecting Slow Application-Layer DoS Attacks With PCA. IRI 2021: 176-183
[c362]Michael Crawford, Taghi M. Khoshgoftaar:
Using Inductive Transfer Learning to Improve Hotel Review Spam Detection. IRI 2021: 248-254
[c361]Richard Zuech, John T. Hancock, Taghi M. Khoshgoftaar:
Detecting Web Attacks in Severely Imbalanced Network Traffic Data. IRI 2021: 267-273
[c360]John T. Hancock, Taghi M. Khoshgoftaar:
Impact of Hyperparameter Tuning in Classifying Highly Imbalanced Big Data. IRI 2021: 348-354
[c359]Justin M. Johnson, Taghi M. Khoshgoftaar:
Encoding Techniques for High-Cardinality Features and Ensemble Learners. IRI 2021: 355-361
[c358]Aline Anacleto, Taghi M. Khoshgoftaar, Evangelos I. Kaisar:
Predicting Traffic Incidents in Road Networks Using Vehicle Detector Data. ITSC 2021: 1431-1436
[c357]Jared M. Peterson, Joffrey L. Leevy, Taghi M. Khoshgoftaar:
A Review and Analysis of the Bot-IoT Dataset. SOSE 2021: 20-27- 2020
[j177]Richard A. Bauder, Taghi M. Khoshgoftaar:
A study on rare fraud predictions with big Medicare claims fraud data. Intell. Data Anal. 24(1): 141-161 (2020)
[j176]Justin M. Johnson
, Taghi M. Khoshgoftaar:
The Effects of Data Sampling with Deep Learning and Highly Imbalanced Big Data. Inf. Syst. Frontiers 22(5): 1113-1131 (2020)
[j175]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy
, Richard A. Bauder:
Investigating class rarity in big data. J. Big Data 7(1): 23 (2020)
[j174]John T. Hancock, Taghi M. Khoshgoftaar:
Survey on categorical data for neural networks. J. Big Data 7(1): 28 (2020)
[j173]Joffrey L. Leevy
, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
Investigating the relationship between time and predictive model maintenance. J. Big Data 7(1): 36 (2020)
[j172]Joffrey L. Leevy
, Taghi M. Khoshgoftaar, Flavio Villanustre
:
Survey on RNN and CRF models for de-identification of medical free text. J. Big Data 7(1): 73 (2020)
[j171]John T. Hancock
, Taghi M. Khoshgoftaar:
CatBoost for big data: an interdisciplinary review. J. Big Data 7(1): 94 (2020)
[j170]Joffrey L. Leevy
, Taghi M. Khoshgoftaar:
A survey and analysis of intrusion detection models based on CSE-CIC-IDS2018 Big Data. J. Big Data 7(1): 104 (2020)
[j169]Aaron N. Richter
, Taghi M. Khoshgoftaar:
Sample size determination for biomedical big data with limited labels. Netw. Model. Anal. Health Informatics Bioinform. 9(1): 12 (2020)
[c356]Joffrey L. Leevy
, John T. Hancock, Richard Zuech, Taghi M. Khoshgoftaar:
Detecting Cybersecurity Attacks Using Different Network Features with LightGBM and XGBoost Learners. CogMI 2020: 190-197
[c355]Joffrey L. Leevy
, Taghi M. Khoshgoftaar:
A Short Survey of LSTM Models for De-identification of Medical Free Text. CIC 2020: 117-124
[c354]Justin M. Johnson, Taghi M. Khoshgoftaar:
Hcpcs2Vec: Healthcare Procedure Embeddings for Medicare Fraud Prediction. CIC 2020: 145-152
[c353]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Evaluating The Number of Trainable Parameters on Deep Maxout and LReLU Networks for Visual Recognition. ICMLA 2020: 415-421
[c352]John T. Hancock, Taghi M. Khoshgoftaar:
Performance of CatBoost and XGBoost in Medicare Fraud Detection. ICMLA 2020: 572-579
[c351]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar:
Accelerated Deep Learning on HPCC Systems. ICMLA 2020: 847-852
[c350]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Detection Methods of Slow Read DoS Using Full Packet Capture Data. IRI 2020: 9-16
[c349]John T. Hancock, Taghi M. Khoshgoftaar:
Medicare Fraud Detection using CatBoost. IRI 2020: 97-103
[c348]Justin M. Johnson, Taghi M. Khoshgoftaar:
Semantic Embeddings for Medical Providers and Fraud Detection. IRI 2020: 224-230
2010 – 2019
- 2019
[j168]Aaron N. Richter, Taghi M. Khoshgoftaar:
Efficient learning from big data for cancer risk modeling: A case study with melanoma. Comput. Biol. Medicine 110: 29-39 (2019)
[j167]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Networks for Visual Recognition. Int. J. Multim. Data Eng. Manag. 10(4): 1-25 (2019)
[j166]Joseph D. Prusa, Ryan Sagul
, Taghi M. Khoshgoftaar:
Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. Inf. Syst. Frontiers 21(1): 109-123 (2019)
[j165]Robert K. L. Kennedy
, Taghi M. Khoshgoftaar, Flavio Villanustre
, Timothy Humphrey:
A parallel and distributed stochastic gradient descent implementation using commodity clusters. J. Big Data 6: 16 (2019)
[j164]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of class rarity on the evaluation of supervised healthcare fraud detection models. J. Big Data 6: 21 (2019)
[j163]Justin M. Johnson, Taghi M. Khoshgoftaar:
Survey on deep learning with class imbalance. J. Big Data 6: 27 (2019)
[j162]Connor Shorten
, Taghi M. Khoshgoftaar:
A survey on Image Data Augmentation for Deep Learning. J. Big Data 6: 60 (2019)
[j161]Justin M. Johnson
, Taghi M. Khoshgoftaar:
Medicare fraud detection using neural networks. J. Big Data 6: 63 (2019)
[j160]Chad Calvert
, Taghi M. Khoshgoftaar:
Impact of class distribution on the detection of slow HTTP DoS attacks using Big Data. J. Big Data 6: 67 (2019)
[j159]Victor M. Herrera
, Taghi M. Khoshgoftaar, Flavio Villanustre
, Borko Furht:
Random forest implementation and optimization for Big Data analytics on LexisNexis's high performance computing cluster platform. J. Big Data 6: 68 (2019)
[j158]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy
, Naeem Seliya:
Examining characteristics of predictive models with imbalanced big data. J. Big Data 6: 69 (2019)
[j157]Gabriel Castaneda
, Paul Morris, Taghi M. Khoshgoftaar:
Evaluation of maxout activations in deep learning across several big data domains. J. Big Data 6: 72 (2019)
[j156]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy
, Richard A. Bauder:
Severely imbalanced Big Data challenges: investigating data sampling approaches. J. Big Data 6: 107 (2019)
[j155]Aaron N. Richter
, Taghi M. Khoshgoftaar:
Melanoma risk modeling from limited positive samples. Netw. Model. Anal. Health Informatics Bioinform. 8(1): 7 (2019)
[c347]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy
, Naeem Seliya:
Investigating Random Undersampling and Feature Selection on Bioinformatics Big Data. BigDataService 2019: 346-356
[c346]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Maxout Neural Network for Big Data Medical Fraud Detection. BigDataService 2019: 357-362
[c345]Stevens Dormezil, Taghi M. Khoshgoftaar, Federica Robinson-Bryant:
Differentiating between Educational Data Mining and Learning Analytics: A Bibliometric Approach. EDM (Workshops) 2019: 17-22
[c344]Gabriel Castaneda, Paul Morris, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Investigation of Maxout Activations on Convolutional Neural Networks for Big Data Text Sentiment Analysis. FLAIRS 2019: 250-256
[c343]Chad Calvert, Clifford Kemp, Taghi M. Khoshgoftaar, Maryam M. Najafabadi:
Detecting Slow HTTP POST DoS Attacks Using Netflow Features. FLAIRS 2019: 387-390
[c342]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Thresholding with Class-Imbalanced Big Data. ICMLA 2019: 755-762
[c341]Aaron N. Richter, Taghi M. Khoshgoftaar:
Learning Curve Estimation with Large Imbalanced Datasets. ICMLA 2019: 763-768
[c340]Huanjing Wang, Taghi M. Khoshgoftaar:
A Study on Software Metric Selection for Software Fault Prediction. ICMLA 2019: 1045-1050
[c339]Joffrey L. Leevy
, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
The Effect of Time on the Maintenance of a Predictive Model. ICMLA 2019: 1891-1896
[c338]Aaron N. Richter, Taghi M. Khoshgoftaar:
Approximating Learning Curves for Imbalanced Big Data with Limited Labels. ICTAI 2019: 237-242
[c337]Chad Calvert, Taghi M. Khoshgoftaar:
Threshold Based Optimization of Performance Metrics with Severely Imbalanced Big Security Data. ICTAI 2019: 1328-1334
[c336]Richard A. Bauder, Matthew Herland, Taghi M. Khoshgoftaar:
Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study. IRI 2019: 9-14
[c335]Tawfiq Hasanin
, Taghi M. Khoshgoftaar, Joffrey L. Leevy
:
A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data. IRI 2019: 83-88
[c334]Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar:
Deep Learning with Maxout Activations for Visual Recognition and Verification. IRI 2019: 135-142
[c333]Justin M. Johnson, Taghi M. Khoshgoftaar:
Deep Learning and Data Sampling with Imbalanced Big Data. IRI 2019: 175-183
[e4]M. Arif Wani, Taghi M. Khoshgoftaar, Dingding Wang, Huanjing Wang, Naeem Seliya:
18th IEEE International Conference On Machine Learning And Applications, ICMLA 2019, Boca Raton, FL, USA, December 16-19, 2019. IEEE 2019, ISBN 978-1-7281-4550-1 [contents]- 2018
[j154]Aaron N. Richter, Taghi M. Khoshgoftaar:
A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artif. Intell. Medicine 90: 1-14 (2018)
[j153]Richard A. Bauder, Taghi M. Khoshgoftaar:
The effects of varying class distribution on learner behavior for medicare fraud detection with imbalanced big data. Health Inf. Sci. Syst. 6(1): 9 (2018)
[j152]Karl R. Weiss, Taghi M. Khoshgoftaar:
A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms. Int. J. Artif. Intell. Tools 27(6): 1850022:1-1850022:33 (2018)
[j151]Sahar Sohangir
, Dingding Wang, Anna Pomeranets, Taghi M. Khoshgoftaar:
Big Data: Deep Learning for financial sentiment analysis. J. Big Data 5: 3 (2018)
[j150]Matthew Herland, Taghi M. Khoshgoftaar, Richard A. Bauder:
Big Data fraud detection using multiple medicare data sources. J. Big Data 5: 29 (2018)
[j149]Joffrey L. Leevy
, Taghi M. Khoshgoftaar, Richard A. Bauder, Naeem Seliya:
A survey on addressing high-class imbalance in big data. J. Big Data 5: 42 (2018)
[j148]Brian Heredia
, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Social media for polling and predicting United States election outcome. Soc. Netw. Anal. Min. 8(1): 48:1-48:16 (2018)
[c332]Aaron N. Richter, Taghi M. Khoshgoftaar:
Melanoma Risk Prediction with Structured Electronic Health Records. BCB 2018: 194-199
[c331]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
The Impact of Malicious Accounts on Political Tweet Sentiment. CIC 2018: 197-202
[c330]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Location-Based Twitter Sentiment Analysis for Predicting the U.S. 2016 Presidential Election. FLAIRS 2018: 265-270
[c329]Richard A. Bauder, Taghi M. Khoshgoftaar, Amri Napolitano:
Fraud Detection with a Limited Number of Known Fraudulent Medicare Providers. FLAIRS 2018: 299-304
[c328]Richard A. Bauder, Taghi M. Khoshgoftaar:
The Detection of Medicare Fraud Using Machine Learning Methods with Excluded Provider Labels. FLAIRS 2018: 404-409
[c327]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin
:
An Empirical Study on Class Rarity in Big Data. ICMLA 2018: 785-790
[c326]Richard A. Bauder, Taghi M. Khoshgoftaar, Tawfiq Hasanin
:
Data Sampling Approaches with Severely Imbalanced Big Data for Medicare Fraud Detection. ICTAI 2018: 137-142
[c325]Aaron N. Richter, Taghi M. Khoshgoftaar:
Building and Interpreting Risk Models from Imbalanced Clinical Data. ICTAI 2018: 143-150
[c324]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Survey of Medicare Data Processing and Integration for Fraud Detection. IRI 2018: 9-14
[c323]Tawfiq Hasanin
, Taghi M. Khoshgoftaar:
The Effects of Random Undersampling with Simulated Class Imbalance for Big Data. IRI 2018: 70-79
[c322]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Random Forest with Class Imbalanced Big Data. IRI 2018: 80-87
[c321]Clifford Kemp, Chad Calvert, Taghi M. Khoshgoftaar:
Utilizing Netflow Data to Detect Slow Read Attacks. IRI 2018: 108-116
[c320]Richard A. Bauder, Raquel da Rosa, Taghi M. Khoshgoftaar:
Identifying Medicare Provider Fraud with Unsupervised Machine Learning. IRI 2018: 285-292
[c319]Ahmad Abu Shanab, Taghi M. Khoshgoftaar:
Is Gene Selection Enough for Imbalanced Bioinformatics Data? IRI 2018: 346-355
[c318]Ahmad Abu Shanab, Taghi M. Khoshgoftaar:
Filter-Based Subset Selection for Easy, Moderate, and Hard Bioinformatics Data. IRI 2018: 372-377- 2017
[j147]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Improving deep neural network design with new text data representations. J. Big Data 4: 7 (2017)
[j146]Maryam M. Najafabadi
, Taghi M. Khoshgoftaar, Flavio Villanustre
, John Holt:
Large-scale distributed L-BFGS. J. Big Data 4: 22 (2017)
[j145]Oscar Day
, Taghi M. Khoshgoftaar:
A survey on heterogeneous transfer learning. J. Big Data 4: 29 (2017)
[j144]Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Mingwen Shao, Wenying Liu:
Reliability Evaluation Model of Component-Based Software Based on Complex Network Theory. Qual. Reliab. Eng. Int. 33(3): 543-550 (2017)
[j143]Brian Heredia
, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Improving detection of untrustworthy online reviews using ensemble learners combined with feature selection. Soc. Netw. Anal. Min. 7(1): 37:1-37:18 (2017)
[c317]Aaron N. Richter, Taghi M. Khoshgoftaar:
Predicting sentinel node status in melanoma from a real-world EHR dataset. BIBM 2017: 1872-1878
[c316]Gabriel Castaneda, Taghi M. Khoshgoftaar:
A Review of Performance Evaluation on 2D Face Databases. BigDataService 2017: 218-223
[c315]Karl R. Weiss, Taghi M. Khoshgoftaar:
Detection of Phishing Webpages Using Heterogeneous Transfer Learning. CIC 2017: 190-197
[c314]Brian Heredia, Joseph D. Prusa, Taghi M. Khoshgoftaar:
Exploring the Effectiveness of Twitter at Polling the United States 2016 Presidential Election. CIC 2017: 283-290
[c313]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
A Text Mining Approach for Anomaly Detection in Application Layer DDoS Attacks. FLAIRS 2017: 312-317
[c312]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Deep Neural Network Architecture for Character-Level Learning on Short Text. FLAIRS 2017: 353-358
[c311]Richard A. Bauder, Taghi M. Khoshgoftaar:
Multivariate Anomaly Detection in Medicare using Model Residuals and Probabilistic Programming. FLAIRS 2017: 417-422
[c310]Karl R. Weiss, Taghi M. Khoshgoftaar:
Comparing Transfer Learning and Traditional Learning Under Domain Class Imbalance. ICMLA 2017: 337-343
[c309]Richard A. Bauder, Taghi M. Khoshgoftaar:
Medicare Fraud Detection Using Machine Learning Methods. ICMLA 2017: 858-865
[c308]Karl R. Weiss, Taghi M. Khoshgoftaar:
Evaluation of Transfer Learning Algorithms Using Different Base Learners. ICTAI 2017: 187-196
[c307]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Training Convolutional Networks on Truncated Text. ICTAI 2017: 330-335
[c306]Joseph D. Prusa, Ryan Sagul, Taghi M. Khoshgoftaar, Michael Sterling:
Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures. IRI 2017: 42-48
[c305]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
User Behavior Anomaly Detection for Application Layer DDoS Attacks. IRI 2017: 154-161
[c304]Karl R. Weiss, Taghi M. Khoshgoftaar:
Analysis of Transfer Learning Performance Measures. IRI 2017: 338-345
[c303]Sara Landset, Michael F. Bergeron, Taghi M. Khoshgoftaar:
Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study. IRI 2017: 366-371
[c302]Aaron N. Richter, Taghi M. Khoshgoftaar:
Modernizing Analytics for Melanoma with a Large-Scale Research Dataset. IRI 2017: 551-558
[c301]Richard A. Bauder, Taghi M. Khoshgoftaar:
Estimating Outlier Score Probabilities. IRI 2017: 559-568
[c300]Matthew Herland, Richard A. Bauder, Taghi M. Khoshgoftaar:
Medical Provider Specialty Predictions for the Detection of Anomalous Medicare Insurance Claims. IRI 2017: 579-588- 2016
[j142]Kewen Li, Lu Liu, Jiannan Zhai, Taghi M. Khoshgoftaar, Timing Li:
The improved grey model based on particle swarm optimization algorithm for time series prediction. Eng. Appl. Artif. Intell. 55: 285-291 (2016)
[j141]Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang:
A survey of transfer learning. J. Big Data 3: 9 (2016)
[c299]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Integrating Multiple Data Sources to Enhance Sentiment Prediction. CIC 2016: 285-291
[c298]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Amri Napolitano, Charles Wheelus:
RUDY Attack: Detection at the Network Level and Its Important Features. FLAIRS 2016: 288-293
[c297]Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa:
Reducing Feature Set Explosion to Facilitate Real-World Review Spam Detection. FLAIRS 2016: 304-309
[c296]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
Enhancing Ensemble Learners with Data Sampling on High-Dimensional Imbalanced Tweet Sentiment Data. FLAIRS 2016: 322-328
[c295]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Comparing Approaches for Combining Data Sampling and Feature Selection to Address Key Data Quality Issues in Tweet Sentiment Analysis. FLAIRS 2016: 608-613
[c294]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Necessity of Feature Selection when Augmenting Tweet Sentiment Feature Spaces with Emoticons. FLAIRS 2016: 614-620
[c293]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
An Investigation of Ensemble Techniques for Detection of Spam Reviews. ICMLA 2016: 127-133
[c292]Karl R. Weiss, Taghi M. Khoshgoftaar:
Investigating Transfer Learners for Robustness to Domain Class Imbalance. ICMLA 2016: 207-213
[c291]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Probabilistic Programming Approach for Outlier Detection in Healthcare Claims. ICMLA 2016: 347-354
[c290]Karl R. Weiss, Taghi M. Khoshgoftaar:
An Investigation of Transfer Learning and Traditional Machine Learning Algorithms. ICTAI 2016: 283-290
[c289]Richard A. Bauder, Taghi M. Khoshgoftaar, Aaron N. Richter, Matthew Herland:
Predicting Medical Provider Specialties to Detect Anomalous Insurance Claims. ICTAI 2016: 784-790
[c288]Richard A. Bauder, Taghi M. Khoshgoftaar:
A Novel Method for Fraudulent Medicare Claims Detection from Expected Payment Deviations (Application Paper). IRI 2016: 11-19
[c287]Karl R. Weiss, Taghi M. Khoshgoftaar, Oneeb Rehman:
Designing a Testing Framework for Transfer Learning Algorithms (Application Paper). IRI 2016: 152-159
[c286]Brian Heredia, Taghi M. Khoshgoftaar, Joseph D. Prusa, Michael Crawford:
Cross-Domain Sentiment Analysis: An Empirical Investigation. IRI 2016: 160-165
[c285]Aaron N. Richter, Taghi M. Khoshgoftaar:
Predicting Cancer Relapse with Clinical Data: A Survey of Current Techniques. IRI 2016: 369-376
[c284]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating the Variation of Ensemble Size on Bagging-Based Classifier Performance in Imbalanced Bioinformatics Datasets. IRI 2016: 377-383
[c283]Joseph D. Prusa, Taghi M. Khoshgoftaar:
Designing a Better Data Representation for Deep Neural Networks and Text Classification. IRI 2016: 411-416
[p5]Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang:
Transfer Learning Techniques. Big Data Technologies and Applications 2016: 53-99
[p4]Maryam M. Najafabadi, Flavio Villanustre
, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep Learning Techniques in Big Data Analytics. Big Data Technologies and Applications 2016: 133-156
[p3]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Is Data Sampling Required When Using Random Forest for Classification on Imbalanced Bioinformatics Data? Theoretical Information Reuse and Integration 2016: 157-171- 2015
[j140]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Investigation on Wrapper-Based Feature Selection for Predicting Software Quality. Int. J. Softw. Eng. Knowl. Eng. 25(1): 93-114 (2015)
[j139]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Investigating Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 25(1): 115-146 (2015)
[j138]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1467-1490 (2015)
[j137]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Aggregating Data Sampling with Feature Subset Selection to Address Skewed Software Defect Data. Int. J. Softw. Eng. Knowl. Eng. 25(9-10): 1531-1550 (2015)
[j136]Maryam M. Najafabadi, Flavio Villanustre
, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, Edin Muharemagic:
Deep learning applications and challenges in big data analytics. J. Big Data 2: 1 (2015)
[j135]Richard Zuech, Taghi M. Khoshgoftaar, Randall Wald:
Intrusion detection and Big Heterogeneous Data: a Survey. J. Big Data 2: 3 (2015)
[j134]Michael Crawford, Taghi M. Khoshgoftaar, Joseph D. Prusa, Aaron N. Richter, Hamzah Al Najada:
Survey of review spam detection using machine learning techniques. J. Big Data 2: 23 (2015)
[j133]Sara Landset, Taghi M. Khoshgoftaar, Aaron N. Richter, Tawfiq Hasanin
:
A survey of open source tools for machine learning with big data in the Hadoop ecosystem. J. Big Data 2: 24 (2015)
[c282]Richard Zuech, Taghi M. Khoshgoftaar, Naeem Seliya, Maryam M. Najafabadi, Clifford Kemp:
A New Intrusion Detection Benchmarking System. FLAIRS 2015: 252-256
[c281]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman:
Impact of Feature Selection Techniques for Tweet Sentiment Classification. FLAIRS 2015: 299-304
[c280]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Ensemble Learning Approach for Balanced Bioinformatics Data. FLAIRS 2015: 329-334
[c279]Joseph D. Prusa, Taghi M. Khoshgoftaar, Naeem Seliya:
The Effect of Dataset Size on Training Tweet Sentiment Classifiers. ICMLA 2015: 96-102
[c278]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Chad Calvert, Clifford Kemp:
Detection of SSH Brute Force Attacks Using Aggregated Netflow Data. ICMLA 2015: 283-288
[c277]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Does the Inclusion of Data Sampling Improve the Performance of Boosting Algorithms on Imbalanced Bioinformatics Data? ICMLA 2015: 527-534
[c276]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Utilizing Ensemble, Data Sampling and Feature Selection Techniques for Improving Classification Performance on Tweet Sentiment Data. ICMLA 2015: 535-542
[c275]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Investigating New Bootstrapping Approaches of Bagging Classifiers to Account for Class Imbalance in Bioinformatics Datasets. ICMLA 2015: 987-994
[c274]Joseph D. Prusa, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Feature Selection in Combination with Ensemble Learning Techniques to Improve Tweet Sentiment Classification Performance. ICTAI 2015: 186-193
[c273]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Ensemble vs. Data Sampling: Which Option Is Best Suited to Improve Classification Performance of Imbalanced Bioinformatics Data? ICTAI 2015: 705-712
[c272]Aaron N. Richter, Michael Crawford, Brian Heredia, Taghi M. Khoshgoftaar:
Efficient Modeling of User-Entity Preference in Big Social Networks. ICTAI 2015: 982-988
[c271]Aaron N. Richter, Taghi M. Khoshgoftaar, Sara Landset, Tawfiq Hasanin
:
A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data. IRI 2015: 1-8
[c270]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Choosing an Appropriate Ensemble Classifier for Balanced Bioinformatics Data. IRI 2015: 17-24
[c269]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Using Random Undersampling to Alleviate Class Imbalance on Tweet Sentiment Data. IRI 2015: 197-202
[c268]Gabriel Castaneda, Taghi M. Khoshgoftaar:
A Survey of 2D Face Databases. IRI 2015: 219-224
[c267]Brian Heredia, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman:
Building an Effective Classification Model for Breast Cancer Patient Response Data. IRI 2015: 229-235
[c266]Joseph D. Prusa, Taghi M. Khoshgoftaar, David J. Dittman:
Using Ensemble Learners to Improve Classifier Performance on Tweet Sentiment Data. IRI 2015: 252-257
[c265]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Alterations to the Bootstrapping Process within Random Forest: A Case Study on Imbalanced Bioinformatics Data. IRI 2015: 342-348
[c264]Alireza Fazelpour, Taghi M. Khoshgoftaar, David J. Dittman, Ahmad Abu Shanab:
Observing the Effect of the Choice of Classifier on Bioinformatics Data with Varying Levels of Data Quality and Class Balance. IRI 2015: 372-379
[c263]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
The Effect of Data Sampling When Using Random Forest on Imbalanced Bioinformatics Data. IRI 2015: 457-463
[c262]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Three Forms of Feature Selection Methods on Software Engineering Data. SEKE 2015: 385-390
[c261]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Combining Feature Subset Selection and Data Sampling for Coping with Highly Imbalanced Software Data. SEKE 2015: 439-444- 2014
[j132]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
The Use of Ensemble-Based Data Preprocessing Techniques for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. 24(9): 1229-1254 (2014)
[j131]Zhiwei Xu
, Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
System regression test planning with a fuzzy expert system. Inf. Sci. 259: 532-543 (2014)
[j130]Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao:
Software quality assessment using a multi-strategy classifier. Inf. Sci. 259: 555-570 (2014)
[j129]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco:
An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Inf. Sci. 259: 571-595 (2014)
[j128]Jason Van Hulse, Taghi M. Khoshgoftaar:
Incomplete-case nearest neighbor imputation in software measurement data. Inf. Sci. 259: 596-610 (2014)
[j127]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano, Randall Wald:
A comparative study of iterative and non-iterative feature selection techniques for software defect prediction. Inf. Syst. Frontiers 16(5): 801-822 (2014)
[j126]Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald:
A review of data mining using big data in health informatics. J. Big Data 1: 2 (2014)
[c260]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Evaluation of Wrapper-Based Feature Selection Using Hard, Moderate, and Easy Bioinformatics Data. BIBE 2014: 149-155
[c259]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Correlation-Based Feature Selection for a Diverse Collection of Bioinformatics Datasets. BIBE 2014: 156-162
[c258]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano:
Selecting the Appropriate Data Sampling Approach for Imbalanced and High-Dimensional Bioinformatics Datasets. BIBE 2014: 304-310
[c257]Randall Wald, Taghi M. Khoshgoftaar, Richard Zuech, Amri Napolitano:
Network Traffic Prediction Models for Near- and Long-Term Predictions. BIBE 2014: 362-368
[c256]Charles Wheelus, Taghi M. Khoshgoftaar, Richard Zuech, Maryam M. Najafabadi:
A Session Based Approach for Aggregating Network Traffic Data - The SANTA Dataset. BIBE 2014: 369-378
[c255]Maryam M. Najafabadi, Taghi M. Khoshgoftaar, Clifford Kemp, Naeem Seliya, Richard Zuech:
Machine Learning for Detecting Brute Force Attacks at the Network Level. BIBE 2014: 379-385
[c254]David J. Dittman, Taghi M. Khoshgoftaar, Amri Napolitano, Alireza Fazelpour:
Select-Bagging: Effectively Combining Gene Selection and Bagging for Balanced Bioinformatics Data. BIBE 2014: 413-419
[c253]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Effects of the Use of Boosting on Classification Performance of Imbalanced Bioinformatics Datasets. BIBE 2014: 420-426
[c252]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of Data Sampling Approaches for Imbalanced Bioinformatics Data. FLAIRS 2014
[c251]Kehan Gao, Taghi M. Khoshgoftaar, Randall Wald:
Combining Feature Selection and Ensemble Learning for Software Quality Estimation. FLAIRS 2014
[c250]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Optimizing Wrapper-Based Feature Selection for Use on Bioinformatics Data. FLAIRS 2014
[c249]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
How ranker and learner choice affects classification performance on noisy bioinformatics data. IRI 2014: 277-282
[c248]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
The effect of noise level and distribution on classification of easy gene microarray data. IRI 2014: 297-302
[c247]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of filter- and wrapper-based software metric selection techniques. IRI 2014: 309-314
[c246]Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman, Amri Napolitano:
Classification performance of three approaches for combining data sampling and gene selection on bioinformatics data. IRI 2014: 315-321
[c245]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Improving software quality estimation by combining feature selection strategies with sampled ensemble learning. IRI 2014: 428-433
[c244]Gabriel Castaneda Oscos, Taghi M. Khoshgoftaar, Randall Wald:
Rotation invariant face recognition survey. IRI 2014: 835-840
[c243]Randall Wald, Flavio Villanustre
, Taghi M. Khoshgoftaar, Richard Zuech, Jarvis Robinson, Edin Muharemagic:
Using feature selection and classification to build effective and efficient firewalls. IRI 2014: 850-854
[c242]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparing Two Approaches for Adding Feature Ranking to Sampled Ensemble Learning for Software Quality Estimation. SEKE 2014: 280-285
[c241]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Choosing the Best Classification Performance Metric for Wrapper-based Software Metric Selection for Defect Prediction. SEKE 2014: 540-545- 2013
[j125]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection for Optimization of Wavelet Packet Decomposition in Reliability Analysis of Systems. Int. J. Artif. Intell. Tools 22(5) (2013)
[j124]Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang:
A Study of Software Metric Selection Techniques: stability Analysis and Defect Prediction Model Performance. Int. J. Artif. Intell. Tools 22(5) (2013)
[j123]Xingquan Zhu
, Taghi M. Khoshgoftaar:
Editorial. Int. J. Artif. Intell. Tools 22(5) (2013)
[j122]Victor L. Winter, Bojan Cukic, Taghi M. Khoshgoftaar, Kinji Mori, Raymond A. Paul, Carlos Pérez Leguízamo, Sahra Sedigh Sarvestani
, John C. Sloan, Mladen A. Vouk, I-Ling Yen:
High Consequence Systems and Semantic Computing. Int. J. Semantic Comput. 7(3): 291-324 (2013)
[c240]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Classification Performance of Rank Aggregation Techniques for Ensemble Gene Selection. FLAIRS 2013
[c239]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
Ensemble Gene Selection Versus Single Gene Selection: Which Is Better? FLAIRS 2013
[c238]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Improving Software Quality Estimation by Combining Boosting and Feature Selection. ICMLA (1) 2013: 27-33
[c237]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on Wrapper-Based Feature Selection for Software Engineering Data. ICMLA (2) 2013: 84-89
[c236]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Amri Napolitano:
Random Forest with 200 Selected Features: An Optimal Model for Bioinformatics Research. ICMLA (1) 2013: 154-160
[c235]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Simplifying the Utilization of Machine Learning Techniques for Bioinformatics. ICMLA (2) 2013: 396-403
[c234]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
Contrasting Undersampled Boosting with Internal and External Feature Selection for Patient Response Datasets. ICMLA (2) 2013: 404-410
[c233]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab, Amri Napolitano:
Comparative Analysis on the Stability of Feature Selection Techniques Using Three Frameworks on Biological Datasets. ICMLA (1) 2013: 418-423
[c232]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Stability for Different Families of Filter-Based and Wrapper-Based Feature Selection. ICMLA (2) 2013: 457-464
[c231]Matthew Herland, Taghi M. Khoshgoftaar, Randall Wald:
Survey of Clinical Data Mining Applications on Big Data in Health Informatics. ICMLA (2) 2013: 465-472
[c230]Liz Aranguren Pachano, Taghi M. Khoshgoftaar, Randall Wald:
Survey of Data Cleansing and Monitoring for Large-Scale Battery Backup Installations. ICMLA (2) 2013: 478-484
[c229]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Which Users Reply to and Interact with Twitter Social Bots? ICTAI 2013: 135-144
[c228]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter- and Wrapper-Based Feature Subset Selection. ICTAI 2013: 374-380
[c227]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Wael Awada:
A Review of Ensemble Classification for DNA Microarrays Data. ICTAI 2013: 381-389
[c226]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
How the Choice of Wrapper Learner and Performance Metric Affects Subset Evaluation. ICTAI 2013: 426-432
[c225]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Should the Same Learners Be Used Both within Wrapper Feature Selection and for Building Classification Models? ICTAI 2013: 439-445
[c224]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Maximizing Classification Performance for Patient Response Datasets. ICTAI 2013: 454-462
[c223]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
Comparison of Two Frameworks for Measuring the Stability of Gene-Selection Techniques on Noisy Class-Imbalanced Data. ICTAI 2013: 881-888
[c222]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Predicting susceptibility to social bots on Twitter. IRI 2013: 6-13
[c221]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
The importance of performance metrics within wrapper feature selection. IRI 2013: 105-111
[c220]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparison of rank-based vs. score-based aggregation for ensemble gene selection. IRI 2013: 225-231
[c219]Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour, David J. Dittman:
Hidden dependencies between class imbalance and difficulty of learning for bioinformatics datasets. IRI 2013: 232-238
[c218]Randall Wald, Taghi M. Khoshgoftaar:
Patient response datasets: Challenges and opportunities. IRI 2013: 254-261
[c217]Taghi M. Khoshgoftaar, Randall Wald, David J. Dittman, Amri Napolitano:
Feature list aggregation approaches for ensemble gene selection on patient response datasets. IRI 2013: 317-324
[c216]Randall Wald, Taghi M. Khoshgoftaar, Alireza Fazelpour:
The use of balance-aware subsampling for bioinformatics datasets. IRI 2013: 325-332
[c215]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Gene selection stability's dependence on dataset difficulty. IRI 2013: 341-348
[c214]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano:
Filter- and wrapper-based feature selection for predicting user interaction with Twitter bots. IRI 2013: 416-423
[c213]Taghi M. Khoshgoftaar, Alireza Fazelpour, Huanjing Wang, Randall Wald:
A survey of stability analysis of feature subset selection techniques. IRI 2013: 424-431
[c212]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Study on First Order Statistics-Based Feature Selection Techniques on Software Metric Data. SEKE 2013: 467-472
[c211]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Ensemble-Based Data Preprocessing Techniques for Software Quality Estimation. SEKE 2013: 612-617
[c210]Taghi M. Khoshgoftaar:
Overcoming Big Data Challenges. SEKE 2013: xxix- 2012
[j121]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Measuring stability of feature ranking techniques: a noise-based approach. Int. J. Bus. Intell. Data Min. 7(1/2): 80-115 (2012)
[j120]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Evaluation of the importance of data pre-processing order when combining feature selection and data sampling. Int. J. Bus. Intell. Data Min. 7(1/2): 116-134 (2012)
[j119]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang:
Exploring filter-based feature selection techniques for software quality classification. Int. J. Inf. Decis. Sci. 4(2/3): 217-250 (2012)
[j118]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
Software measurement data reduction using ensemble techniques. Neurocomputing 92: 124-132 (2012)
[j117]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Feature Ranking Techniques for Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. 22(2): 161-183 (2012)
[j116]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Threshold-based feature selection techniques for high-dimensional bioinformatics data. Netw. Model. Anal. Health Informatics Bioinform. 1(1-2): 47-61 (2012)
[j115]Ankur Agarwal, Georgiana L. Hamza-Lup, Taghi M. Khoshgoftaar:
A System-Level Modeling Methodology for Performance-Driven Component Selection in Multicore Architectures. IEEE Syst. J. 6(2): 317-328 (2012)
[j114]Kehan Gao, Taghi M. Khoshgoftaar, Naeem Seliya:
Predicting high-risk program modules by selecting the right software measurements. Softw. Qual. J. 20(1): 3-42 (2012)
[c209]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Similarity analysis of feature ranking techniques on imbalanced DNA microarray datasets. BIBM 2012: 1-5
[c208]Randall Wald, Taghi M. Khoshgoftaar, Ahmad Abu Shanab:
The effect of measurement approach and noise level on gene selection stability. BIBM 2012: 1-5
[c207]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Robustness of Threshold-Based Feature Rankers with Data Sampling on Noisy and Imbalanced Data. FLAIRS 2012
[c206]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
Mean Aggregation versus Robust Rank Aggregation for Ensemble Gene Selection. ICMLA (1) 2012: 63-69
[c205]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Determining the Number of Iterations Appropriate for Ensemble Gene Selection on Microarray Data. ICMLA (1) 2012: 82-89
[c204]Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Alireza Fazelpour:
First Order Statistics Based Feature Selection: A Diverse and Powerful Family of Feature Seleciton Techniques. ICMLA (2) 2012: 151-157
[c203]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman:
A New Fixed-Overlap Partitioning Algorithm for Determining Stability of Bioinformatics Gene Rankers. ICMLA (2) 2012: 170-177
[c202]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach. ICMLA (2) 2012: 184-191
[c201]Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald:
The Effect of Number of Iterations on Ensemble Gene Selection. ICMLA (2) 2012: 198-203
[c200]Janell Duhaney, Taghi M. Khoshgoftaar, Amri Napolitano:
Studying the Effect of Class Imbalance in Ocean Turbine Fault Data on Reliable State Detection. ICMLA (1) 2012: 268-275
[c199]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction. ICMLA (2) 2012: 281-288
[c198]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A Comparative Study on the Stability of Software Metric Selection Techniques. ICMLA (2) 2012: 301-307
[c197]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study on the Stability of Feature Selection for Imbalanced Software Engineering Data. ICMLA (1) 2012: 317-323
[c196]Janell Duhaney, Taghi M. Khoshgoftaar, Randall Wald:
Applying Feature Selection to Short Time Wavelet Transformed Vibration Data for Reliability Analysis of an Ocean Turbine. ICMLA (1) 2012: 330-337
[c195]Randall Wald, Taghi M. Khoshgoftaar, Amri Napolitano, Chris Sumner:
Using Twitter Content to Predict Psychopathy. ICMLA (2) 2012: 394-401
[c194]Janell Duhaney, Taghi M. Khoshgoftaar:
Decision Level Fusion of Wavelet Features for Ocean Turbine State Detection. ICMLA (2) 2012: 531-537
[c193]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data. ICMLA (2) 2012: 551-557
[c192]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
A novel dataset-similarity-aware approach for evaluating stability of software metric selection techniques. IRI 2012: 1-8
[c191]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
Exploring an iterative feature selection technique for highly imbalanced data sets. IRI 2012: 101-108
[c190]Wael Awada, Taghi M. Khoshgoftaar, David J. Dittman, Randall Wald, Amri Napolitano:
A review of the stability of feature selection techniques for bioinformatics data. IRI 2012: 356-363
[c189]Gordon K. Lee, Elisa Bertino, Stuart Harvey Rubin, Taghi M. Khoshgoftaar, Bhavani Thuraisingham, James D. McCaffrey:
Panel: Using information re-use and integration principles in big data. IRI 2012
[c188]Randall Wald, Taghi M. Khoshgoftaar, Chris Sumner:
Machine prediction of personality from Facebook profiles. IRI 2012: 109-115
[c187]Randall Wald, Taghi M. Khoshgoftaar, David J. Dittman, Wael Awada, Amri Napolitano:
An extensive comparison of feature ranking aggregation techniques in bioinformatics. IRI 2012: 377-384
[c186]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Impact of noise and data sampling on stability of feature ranking techniques for biological datasets. IRI 2012: 415-422
[c185]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Stability of Filter-Based Feature Selection Methods for Imbalanced Software Measurement Data. SEKE 2012: 74-79
[c184]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
An Empirical Study of Software Metric Selection Techniques for Defect Prediction. SEKE 2012: 94-99- 2011
[j113]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An exploration of learning when data is noisy and imbalanced. Intell. Data Anal. 15(2): 215-236 (2011)
[j112]Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse, Kehan Gao:
Metric Selection for Software Defect Prediction. Int. J. Softw. Eng. Knowl. Eng. 21(2): 237-257 (2011)
[j111]Alex Kotlarchyk, Taghi M. Khoshgoftaar, Mirjana Pavlovic, Hanqi Zhuang, Abhijit S. Pandya:
Identification of microRNA biomarkers for cancer by combining multiple feature selection techniques. J. Comput. Methods Sci. Eng. 11(5-6): 283-298 (2011)
[j110]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the Impact of Data Quality on Sampling. J. Inf. Knowl. Manag. 10(3): 225-245 (2011)
[j109]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang, Naeem Seliya:
Choosing software metrics for defect prediction: an investigation on feature selection techniques. Softw. Pract. Exp. 41(5): 579-606 (2011)
[j108]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Comparing Boosting and Bagging Techniques With Noisy and Imbalanced Data. IEEE Trans. Syst. Man Cybern. Part A 41(3): 552-568 (2011)
[j107]Qianhui Liang, Xindong Wu, E. K. Park, Taghi M. Khoshgoftaar, Chi-Hung Chi:
Ontology-Based Business Process Customization for Composite Web Services. IEEE Trans. Syst. Man Cybern. Part A 41(4): 717-729 (2011)
[j106]Naeem Seliya, Taghi M. Khoshgoftaar:
The use of decision trees for cost-sensitive classification: an empirical study in software quality prediction. WIREs Data Mining Knowl. Discov. 1(5): 448-459 (2011)
[c183]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Huanjing Wang:
Stability Analysis of Feature Ranking Techniques on Biological Datasets. BIBM 2011: 252-256
[c182]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Amri Napolitano:
Random forest: A reliable tool for patient response prediction. BIBM Workshops 2011: 289-296
[c181]Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse:
Robustness of Filter-Based Feature Ranking: A Case Study. FLAIRS 2011
[c180]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Level Sensor Fusion for Improved Fault Detection in MCM Systems for Ocean Turbines. FLAIRS 2011
[c179]Huanjing Wang, Taghi M. Khoshgoftaar, Naeem Seliya:
How Many Software Metrics Should be Selected for Defect Prediction? FLAIRS 2011
[c178]John C. Sloan, Taghi M. Khoshgoftaar:
Ensemble Coordination for Discrete Event Control. HASE 2011: 227-235
[c177]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Using Feature Selection to Determine Optimal Depth for Wavelet Packet Decomposition of Vibration Signals for Ocean System Reliability. HASE 2011: 236-243
[c176]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan, Bassem Alhalabi, Pierre P. Beaujean:
A Dynamometer for an Ocean Turbine Prototype: Reliability through Automated Monitoring. HASE 2011: 244-251
[c175]Huanjing Wang, Taghi M. Khoshgoftaar, Qianhui Althea Liang:
Stability and Classification Performance of Feature Selection Techniques. ICMLA (1) 2011: 151-156
[c174]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald:
Impact of Noise and Data Sampling on Stability of Feature Selection. ICMLA (1) 2011: 172-177
[c173]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection for Vibration Sensor Data Transformed by a Streaming Wavelet Packet Decomposition. ICTAI 2011: 978-985
[c172]Huanjing Wang, Taghi M. Khoshgoftaar:
Measuring Stability of Threshold-Based Feature Selection Techniques. ICTAI 2011: 986-993
[c171]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Impact of Data Sampling on Stability of Feature Selection for Software Measurement Data. ICTAI 2011: 1004-1011
[c170]Janell Duhaney, Taghi M. Khoshgoftaar, John C. Sloan:
Feature Selection on Dynamometer Data for Reliability Analysis. ICTAI 2011: 1012-1019
[c169]Ahmad Abu Shanab, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Comparison of approaches to alleviate problems with high-dimensional and class-imbalanced data. IRI 2011: 234-239
[c168]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
A noise-based stability evaluation of threshold-based feature selection techniques. IRI 2011: 240-245
[c167]Huanjing Wang, Taghi M. Khoshgoftaar, Randall Wald:
Measuring robustness of Feature Selection techniques on software engineering datasets. IRI 2011: 309-314
[c166]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A comparative evaluation of feature ranking methods for high dimensional bioinformatics data. IRI 2011: 315-320
[c165]Randall Wald, Taghi M. Khoshgoftaar, John C. Sloan:
Fourier transforms for vibration analysis: A review and case study. IRI 2011: 366-371
[c164]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Amri Napolitano:
Using Classifier-Based Nominal Imputation to Improve Machine Learning. PAKDD (1) 2011: 124-135
[c163]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
A Comparative Study of Different Strategies for Predicting Software Quality. SEKE 2011: 65-70
[c162]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Study of Software Metrics Selection Using Support Vector Machine. SEKE 2011: 83-88
[c161]Kehan Gao, Taghi M. Khoshgoftaar:
Software Defect Prediction for High-Dimensional and Class-Imbalanced Data. SEKE 2011: 89-94
[e3]Taghi M. Khoshgoftaar:
13th IEEE International Symposium on High-Assurance Systems Engineering, HASE 2011, Boca Raton, FL, USA, November 10-12, 2011. IEEE Computer Society 2011, ISBN 978-1-4673-0107-7 [contents]- 2010
[j105]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
Evolutionary data analysis for the class imbalance problem. Intell. Data Anal. 14(1): 69-88 (2010)
[j104]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Evaluation of Repetitive Undersampling Techniques. Int. J. Softw. Eng. Knowl. Eng. 20(2): 173-195 (2010)
[j103]Lianfen Qian
, Qingchuan Yao, Taghi M. Khoshgoftaar:
Dynamic Two-phase Truncated Rayleigh Model for Release Date Prediction of Software. J. Softw. Eng. Appl. 3(6): 603-609 (2010)
[j102]Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors. IEEE Trans. Neural Networks 21(5): 813-830 (2010)
[j101]Yi Liu, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Optimization of Software Quality Modeling with Multiple Repositories. IEEE Trans. Software Eng. 36(6): 852-864 (2010)
[j100]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. IEEE Trans. Syst. Man Cybern. Part A 40(1): 185-197 (2010)
[c160]Taghi M. Khoshgoftaar, Kehan Gao, Amri Napolitano:
An Empirical Study of Predictive Modeling Techniques of Software Quality. BIONETICS 2010: 288-302
[c159]Kehan Gao, Taghi M. Khoshgoftaar, Jason Van Hulse:
An Evaluation of Sampling on Filter-Based Feature Selection Methods. FLAIRS 2010
[c158]Huanjing Wang, Taghi M. Khoshgoftaar, Jason Van Hulse:
A Comparative Study of Threshold-Based Feature Selection Techniques. GrC 2010: 499-504
[c157]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Predicting Faults in High Assurance Software. HASE 2010: 26-34
[c156]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
A Novel Noise Filtering Algorithm for Imbalanced Data. ICMLA 2010: 9-14
[c155]Huanjing Wang, Taghi M. Khoshgoftaar, Amri Napolitano:
A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction. ICMLA 2010: 135-140
[c154]David J. Dittman, Taghi M. Khoshgoftaar, Randall Wald, Jason Van Hulse:
Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques. ICMLA 2010: 147-152
[c153]Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. ICTAI (1) 2010: 137-144
[c152]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Evaluating the impact of data quality on sampling. IRI 2010: 31-36
[c151]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao:
A comparative study of filter-based feature ranking techniques. IRI 2010: 43-48
[c150]Naeem Seliya, Taghi M. Khoshgoftaar:
Active learning with neural networks for intrusion detection. IRI 2010: 49-54
[c149]Taghi M. Khoshgoftaar, Kehan Gao, Jason Van Hulse:
A novel feature selection technique for highly imbalanced data. IRI 2010: 80-85
[c148]Taghi M. Khoshgoftaar, Kehan Gao:
Software Engineering with Computational Intelligence and Machine Learning A Novel Software Metric Selection Technique Using the Area Under ROC Curves. SEKE 2010: 203-208
[c147]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao:
Ensemble Feature Selection Technique for Software Quality Classification. SEKE 2010: 215-220
[e2]Sorin Draghici, Taghi M. Khoshgoftaar, Vasile Palade, Witold Pedrycz, M. Arif Wani, Xingquan Zhu:
The Ninth International Conference on Machine Learning and Applications, ICMLA 2010, Washington, DC, USA, 12-14 December 2010. IEEE Computer Society 2010, ISBN 978-0-7695-4300-0 [contents]
2000 – 2009
- 2009
[j99]Xiaoyuan Su, Taghi M. Khoshgoftaar:
A Survey of Collaborative Filtering Techniques. Adv. Artif. Intell. 2009: 421425:1-421425:19 (2009)
[j98]Jason Van Hulse, Taghi M. Khoshgoftaar:
Knowledge discovery from imbalanced and noisy data. Data Knowl. Eng. 68(12): 1513-1542 (2009)
[j97]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Hybrid sampling for imbalanced data. Integr. Comput. Aided Eng. 16(3): 193-210 (2009)
[j96]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Making an accurate classifier ensemble by voting on classifications from imputed learning sets. Int. J. Inf. Decis. Sci. 1(3): 301-322 (2009)
[j95]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Identifying Learners Robust to Low Quality Data. Informatica (Slovenia) 33(3): 245-259 (2009)
[j94]Taghi M. Khoshgoftaar, Pierre Rebours, Naeem Seliya:
Software quality analysis by combining multiple projects and learners. Softw. Qual. J. 17(1): 25-49 (2009)
[j93]John C. Sloan, Taghi M. Khoshgoftaar:
From Web Service Artifact to a Readable and Verifiable Model. IEEE Trans. Serv. Comput. 2(4): 277-288 (2009)
[j92]Taghi M. Khoshgoftaar, Jason Van Hulse:
Empirical Case Studies in Attribute Noise Detection. IEEE Trans. Syst. Man Cybern. Part C 39(4): 379-388 (2009)
[j91]Dennis J. Drown, Taghi M. Khoshgoftaar, Naeem Seliya:
Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems. IEEE Trans. Syst. Man Cybern. Part A 39(5): 1097-1107 (2009)
[j90]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Improving Software-Quality Predictions With Data Sampling and Boosting. IEEE Trans. Syst. Man Cybern. Part A 39(6): 1283-1294 (2009)
[c146]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
VipBoost: A More Accurate Boosting Algorithm. FLAIRS 2009
[c145]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano, Randall Wald:
Feature Selection with High-Dimensional Imbalanced Data. ICDM Workshops 2009: 507-514
[c144]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
Mining Data from Multiple Software Development Projects. ICDM Workshops 2009: 551-557
[c143]Taghi M. Khoshgoftaar, Kehan Gao:
Feature Selection with Imbalanced Data for Software Defect Prediction. ICMLA 2009: 235-240
[c142]Wilker Altidor, Taghi M. Khoshgoftaar, Amri Napolitano:
Wrapper-Based Feature Ranking for Software Engineering Metrics. ICMLA 2009: 241-246
[c141]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
A Study on the Relationships of Classifier Performance Metrics. ICTAI 2009: 59-66
[c140]Kehan Gao, Taghi M. Khoshgoftaar, Amri Napolitano:
Exploring Software Quality Classification with a Wrapper-Based Feature Ranking Technique. ICTAI 2009: 67-74
[c139]Wilker Altidor, Taghi M. Khoshgoftaar, Jason Van Hulse:
An Empirical Study on Wrapper-Based Feature Ranking. ICTAI 2009: 75-82
[c138]Huanjing Wang, Taghi M. Khoshgoftaar, Kehan Gao, Naeem Seliya:
High-Dimensional Software Engineering Data and Feature Selection. ICTAI 2009: 83-90
[c137]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
An Empirical Comparison of Repetitive Undersampling Techniques. IRI 2009: 29-34
[c136]Naeem Seliya, Taghi M. Khoshgoftaar, Jason Van Hulse:
Aggregating Performance Metrics for Classifier Evaluation. IRI 2009: 35-40
[c135]Kehan Gao, Taghi M. Khoshgoftaar, Huanjing Wang:
An Empirical Investigation of Filter Attribute Selection Techniques for Software Quality Classification. IRI 2009: 272-277
[c134]Pengpeng Lin, Huanjing Wang, Taghi M. Khoshgoftaar:
A Novel Hybrid Search Algorithm for Feature Selection. SEKE 2009: 81-86
[c133]Naeem Seliya, Taghi M. Khoshgoftaar:
Value-Based Software Quality Modeling. SEKE 2009: 116-121
[c132]John C. Sloan, Taghi M. Khoshgoftaar, Augusto Varas:
An Extendible Translation of BPEL to a Machine-verifiable Model. SEKE 2009: 344-349
[r1]Kehan Gao, Taghi M. Khoshgoftaar:
Count Models for Software Quality Estimation. Encyclopedia of Data Warehousing and Mining 2009: 346-352- 2008
[j89]John C. Sloan, Taghi M. Khoshgoftaar, Venkat Raghav:
Assuring Timeliness in an e-Science Service-Oriented Architecture. Computer 41(8): 56-62 (2008)
[j88]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Collaborative Filtering for Multi-Class Data Using Bayesian Networks. Int. J. Artif. Intell. Tools 17(1): 71-85 (2008)
[j87]Taghi M. Khoshgoftaar, Naeem Seliya, Chris Seiffert:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. Int. J. Artif. Intell. Tools 17(3): 521-537 (2008)
[j86]Jason Van Hulse, Taghi M. Khoshgoftaar:
A comprehensive empirical evaluation of missing value imputation in noisy software measurement data. J. Syst. Softw. 81(5): 691-708 (2008)
[j85]Taghi M. Khoshgoftaar, Jason Van Hulse:
Imputation techniques for multivariate missingness in software measurement data. Softw. Qual. J. 16(4): 563-600 (2008)
[c131]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard:
Software quality modeling: The impact of class noise on the random forest classifier. IEEE Congress on Evolutionary Computation 2008: 3853-3859
[c130]Chinar C. Shah, Xingquan Zhu, Taghi M. Khoshgoftaar, Justin Beyer:
Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction. FLAIRS 2008: 95-100
[c129]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Building Useful Models from Imbalanced Data with Sampling and Boosting. FLAIRS 2008: 306-311
[c128]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
A Mixture Imputation-Boosted Collaborative Filter. FLAIRS 2008: 312-316
[c127]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
A Comparative Study of Data Sampling and Cost Sensitive Learning. ICDM Workshops 2008: 46-52
[c126]Andres Folleco, Taghi M. Khoshgoftaar, Amri Napolitano:
Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. ICMLA 2008: 153-158
[c125]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
RUSBoost: Improving classification performance when training data is skewed. ICPR 2008: 1-4
[c124]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu:
VoB predictors: Voting on bagging classifications. ICPR 2008: 1-4
[c123]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Using Imputation Techniques to Help Learn Accurate Classifiers. ICTAI (1) 2008: 437-444
[c122]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Resampling or Reweighting: A Comparison of Boosting Implementations. ICTAI (1) 2008: 445-451
[c121]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Improving Learner Performance with Data Sampling and Boosting. ICTAI (1) 2008: 452-459
[c120]Naeem Seliya, Zhiwei Xu
, Taghi M. Khoshgoftaar:
Addressing Class Imbalance in Non-binary Classification Problems. ICTAI (1) 2008: 460-466
[c119]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Lofton A. Bullard:
Identifying learners robust to low quality data. IRI 2008: 190-195
[c118]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse:
Hybrid sampling for imbalanced data. IRI 2008: 202-207
[c117]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
:
VCI predictors: Voting on classifications from imputed learning sets. IRI 2008: 296-301
[c116]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
, Russell Greiner:
Imputation-boosted collaborative filtering using machine learning classifiers. SAC 2008: 949-950
[c115]Andres Folleco, Taghi M. Khoshgoftaar, Lofton A. Bullard:
Analyzing the Impact of Attribute Noise on Software Quality Classification. SEKE 2008: 73-78
[c114]Taghi M. Khoshgoftaar, Naeem Seliya, Dennis J. Drown:
On the Rarity of Fault-prone Modules in Knowledge-based Software Quality Modeling. SEKE 2008: 279-284
[c113]John C. Sloan, Taghi M. Khoshgoftaar:
Toward Model Checking Web Services Over the Web. SEKE 2008: 519-524
[c112]Xiaoyuan Su, Taghi M. Khoshgoftaar, Russell Greiner:
Imputed Neighborhood Based Collaborative Filtering. Web Intelligence 2008: 633-639
[p2]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Module Risk Analysis. Wiley Encyclopedia of Computer Science and Engineering 2008
[p1]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Modeling as a Reliability Tool. Wiley Encyclopedia of Computer Science and Engineering 2008- 2007
[j84]Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Lili Zhao:
The multiple imputation quantitative noise corrector. Intell. Data Anal. 11(3): 245-263 (2007)
[j83]Taghi M. Khoshgoftaar, Pierre Rebours:
Improving Software Quality Prediction by Noise Filtering Techniques. J. Comput. Sci. Technol. 22(3): 387-396 (2007)
[j82]Xingquan Zhu
, Taghi M. Khoshgoftaar, Ian Davidson, Shichao Zhang:
Editorial: Special issue on mining low-quality data. Knowl. Inf. Syst. 11(2): 131-136 (2007)
[j81]Jason Van Hulse, Taghi M. Khoshgoftaar, Haiying Huang:
The pairwise attribute noise detection algorithm. Knowl. Inf. Syst. 11(2): 171-190 (2007)
[j80]Naeem Seliya, Taghi M. Khoshgoftaar:
Software quality estimation with limited fault data: a semi-supervised learning perspective. Softw. Qual. J. 15(3): 327-344 (2007)
[j79]Taghi M. Khoshgoftaar, Kehan Gao:
Count Models for Software Quality Estimation. IEEE Trans. Reliab. 56(2): 212-222 (2007)
[j78]Kehan Gao, Taghi M. Khoshgoftaar:
A Comprehensive Empirical Study of Count Models for Software Fault Prediction. IEEE Trans. Reliab. 56(2): 223-236 (2007)
[j77]Taghi M. Khoshgoftaar, Yi Liu:
A Multi-Objective Software Quality Classification Model Using Genetic Programming. IEEE Trans. Reliab. 56(2): 237-245 (2007)
[j76]Naeem Seliya, Taghi M. Khoshgoftaar:
Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering. IEEE Trans. Syst. Man Cybern. Part A 37(2): 201-211 (2007)
[c111]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Low-Effort Labeling of Network Events for Intrusion Detection in WLANs. FLAIRS 2007: 490-495
[c110]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Skewed Class Distributions and Mislabeled Examples. ICDM Workshops 2007: 477-482
[c109]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Arbitrarily-Shaped Window Based Stereo Matching using the Go-Light Optimization Algorithm. ICIP (6) 2007: VI:556-559
[c108]Jason Van Hulse, Taghi M. Khoshgoftaar, Amri Napolitano:
Experimental perspectives on learning from imbalanced data. ICML 2007: 935-942
[c107]Lofton A. Bullard, Taghi M. Khoshgoftaar, Kehan Gao:
An application of a rule-based model in software quality classification. ICMLA 2007: 204-210
[c106]Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse, Amri Napolitano, Andres Folleco:
Learning with limited minority class data. ICMLA 2007: 348-353
[c105]Dennis J. Drown, Taghi M. Khoshgoftaar, Ramaswamy Narayanan:
Using evolutionary sampling to mine imbalanced data. ICMLA 2007: 363-368
[c104]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano:
Mining Data with Rare Events: A Case Study. ICTAI (2) 2007: 132-139
[c103]Taghi M. Khoshgoftaar, Moiz Golawala, Jason Van Hulse:
An Empirical Study of Learning from Imbalanced Data Using Random Forest. ICTAI (2) 2007: 310-317
[c102]Xingquan Zhu, Xindong Wu, Taghi M. Khoshgoftaar, Yong Shi:
An Empirical Study of the Noise Impact on Cost-Sensitive Learning. IJCAI 2007: 1168-1174
[c101]Qiming Luo, Taghi M. Khoshgoftaar:
An Empirical Study on Estimating Motions in Video Stabilization. IRI 2007: 360-366
[c100]Jason Van Hulse, Taghi M. Khoshgoftaar:
Incomplete-Case Nearest Neighbor Imputation in Software Measurement Data. IRI 2007: 630-637
[c99]Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung JF Yao:
Building a Novel GP-Based Software Quality Classifier Using Multiple Validation Datasets. IRI 2007: 644-650
[c98]Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Andres Folleco:
An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. IRI 2007: 651-658
[c97]Xiaoyuan Su, Taghi M. Khoshgoftaar:
A Progressive Edge-Based Stereo Correspondence Method. ISVC (1) 2007: 248-257
[c96]Xiaoyuan Su, Taghi M. Khoshgoftaar, Xingquan Zhu
, Andres Folleco:
Rule-Based Multiple Object Tracking for Traffic Surveillance Using Collaborative Background Extraction. ISVC (2) 2007: 469-478
[c95]Andres Folleco, Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert:
Learning from Software Quality Data with Class Imbalance and Noise. SEKE 2007: 487-
[c94]Xiaoyuan Su, Russell Greiner, Taghi M. Khoshgoftaar, Xingquan Zhu:
Hybrid Collaborative Filtering Algorithms Using a Mixture of Experts. Web Intelligence 2007: 645-649- 2006
[j75]Pierre Rebours, Taghi M. Khoshgoftaar:
Quality Problem in Software Measurement Data. Adv. Comput. 66: 44-79 (2006)
[j74]Taghi M. Khoshgoftaar, Jason Van Hulse:
Determining noisy instances relative to attributes of interest. Intell. Data Anal. 10(3): 251-268 (2006)
[j73]Jason Van Hulse, Taghi M. Khoshgoftaar:
Class noise detection using frequent itemsets. Intell. Data Anal. 10(6): 487-507 (2006)
[j72]Taghi M. Khoshgoftaar, Kehan Gao, Hua Lin:
Indirect classification approaches: a comparative study in network intrusion detection. Int. J. Comput. Appl. Technol. 27(4): 232-245 (2006)
[j71]Taghi M. Khoshgoftaar, Pierre Rebours:
Noise elimination with partitioning filter for software quality estimation. Int. J. Comput. Appl. Technol. 27(4): 246-258 (2006)
[j70]Taghi M. Khoshgoftaar, Vedang H. Joshi, Naeem Seliya:
Detecting Noisy Instances with the Ensemble Filter: a Study in Software Quality Estimation. Int. J. Softw. Eng. Knowl. Eng. 16(1): 53-76 (2006)
[j69]Taghi M. Khoshgoftaar, Naeem Seliya, Nandini Sundaresh:
An empirical study of predicting software faults with case-based reasoning. Softw. Qual. J. 14(2): 85-111 (2006)
[j68]Taghi M. Khoshgoftaar, Angela Herzberg, Naeem Seliya:
Resource oriented selection of rule-based classification models: An empirical case study. Softw. Qual. J. 14(4): 309-338 (2006)
[j67]Qiming Luo, Taghi M. Khoshgoftaar:
Unsupervised multiscale color image segmentation based on MDL principle. IEEE Trans. Image Process. 15(9): 2755-2761 (2006)
[c93]Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert:
A Comparison of Software Fault Imputation Procedures. ICMLA 2006: 135-142
[c92]Xiaoyuan Su, Taghi M. Khoshgoftaar:
Collaborative Filtering for Multi-class Data Using Belief Nets Algorithms. ICTAI 2006: 497-504
[c91]Taghi M. Khoshgoftaar, Kehan Gao:
Assessment of a Multi-Strategy Classifier for an Embedded Software System. ICTAI 2006: 651-658
[c90]Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert:
A Hybrid Approach to Cleansing Software Measurement Data. ICTAI 2006: 713-722
[c89]Taghi M. Khoshgoftaar, Chris Seiffert, Naeem Seliya:
Labeling network event records for intrusion detection in a Wireless LAN. IRI 2006: 200-206
[c88]Yi Liu, Taghi M. Khoshgoftaar, Jenq-Foung JF Yao:
Developing an effective validation strategy for genetic programming models based on multiple datasets. IRI 2006: 232-237
[c87]Qiming Luo, Taghi M. Khoshgoftaar, Andres Folleco:
Classification of ships in surveillance video. IRI 2006: 432-437
[c86]Jason Van Hulse, Taghi M. Khoshgoftaar, Chris Seiffert, Lili Zhao:
Noise correction using bayesian multiple imputation. IRI 2006: 478-483
[c85]Taghi M. Khoshgoftaar, Andres Folleco, Jason Van Hulse, Lofton A. Bullard:
Software quality imputation in the presence of noisy data. IRI 2006: 484-489
[c84]Taghi M. Khoshgoftaar, Jason Van Hulse:
Multiple Imputation of Software Measurement Data: A Case Study. SEKE 2006: 220-226
[c83]Taghi M. Khoshgoftaar, Chris Seiffert, Jason Van Hulse:
Polishing Noise in Continuous Software Measurement Data. SEKE 2006: 227-231- 2005
[j66]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study. Empir. Softw. Eng. 10(2): 183-218 (2005)
[j65]Taghi M. Khoshgoftaar, Shi Zhong, Vedang H. Joshi:
Enhancing software quality estimation using ensemble-classifier based noise filtering. Intell. Data Anal. 9(1): 3-27 (2005)
[j64]Taghi M. Khoshgoftaar, Kehan Gao, Nawal H. Ibrahim:
Evaluating indirect and direct classification techniques for network intrusion detection. Intell. Data Anal. 9(3): 309-326 (2005)
[j63]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Detecting noisy instances with the rule-based classification model. Intell. Data Anal. 9(4): 347-364 (2005)
[j62]Taghi M. Khoshgoftaar, Pierre Rebours:
Evaluating noise elimination techniques for software quality estimation. Intell. Data Anal. 9(5): 487-508 (2005)
[j61]Taghi M. Khoshgoftaar, Jason Van Hulse:
Identifying noisy features with the Pairwise Attribute Noise Detection Algorithm. Intell. Data Anal. 9(6): 589-602 (2005)
[j60]Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo:
Comparing software fault predictions of pure and zero-inflated Poisson regression models. Int. J. Syst. Sci. 36(11): 705-715 (2005)
[j59]Taghi M. Khoshgoftaar, Naeem Seliya, Angela Herzberg:
Resource-oriented software quality classification models. J. Syst. Softw. 76(2): 111-126 (2005)
[c82]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Analyzing Software Quality with Limited Fault-Proneness Defect Data. HASE 2005: 89-98
[c81]Taghi M. Khoshgoftaar, Jason Van Hulse:
Identifying noise in an attribute of interest. ICMLA 2005
[c80]Taghi M. Khoshgoftaar, Shyam Varan Nath, Shi Zhong, Naeem Seliya:
Intrusion detection in wireless networks using clustering techniques with expert analysis. ICMLA 2005
[c79]Shi Zhong, Taghi M. Khoshgoftaar, Shyam Varan Nath:
A Clustering Approach to Wireless Network Intrusion Detection. ICTAI 2005: 190-196
[c78]Zhiwei Xu
, Kehan Gao, Taghi M. Khoshgoftaar:
Application of fuzzy expert system in test case selection for system regression test. IRI 2005: 120-125
[c77]Yudong Xiao, Taghi M. Khoshgoftaar, Naeem Seliya:
The partitioning- and rule-based filter for noise detection. IRI 2005: 205-210
[c76]Taghi M. Khoshgoftaar, Jason Van Hulse:
Empirical case studies in attribute noise detection. IRI 2005: 211-216
[c75]Qiming Luo, Taghi M. Khoshgoftaar, Edgar An:
Hierarchical indexing of ocean survey video by mean shift clustering and MDL principle. IRI 2005: 404-409
[e1]Du Zhang, Taghi M. Khoshgoftaar, Mei-Ling Shyu:
Proceedings of the 2005 IEEE International Conference on Information Reuse and Integration, IRI - 2005, August 15-17, 2005, Las Vegas Hilton, Las Vegas, NV, USA. IEEE Systems, Man, and Cybernetics Society 2005, ISBN 0-7803-9093-8 [contents]- 2004
[j58]Taghi M. Khoshgoftaar, Naeem Seliya:
Software quality estimation with case-based reasoning. Adv. Comput. 62: 249-291 (2004)
[j57]Taghi M. Khoshgoftaar, Naeem Seliya:
Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study. Empir. Softw. Eng. 9(3): 229-257 (2004)
[j56]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Analyzing Software Measurement Data with Clustering Techniques. IEEE Intell. Syst. 19(2): 20-27 (2004)
[j55]Zhiwei Xu
, Taghi M. Khoshgoftaar:
Identification of fuzzy models of software cost estimation. Fuzzy Sets Syst. 145(1): 141-163 (2004)
[j54]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
A multiobjective module-order model for software quality enhancement. IEEE Trans. Evol. Comput. 8(6): 593-608 (2004)
[c74]Yi Liu, Taghi M. Khoshgoftaar:
Reducing Overfitting in Genetic Programming Models for Software Quality Classification. HASE 2004: 56-65
[c73]Shi Zhong, Taghi M. Khoshgoftaar, Naeem Seliya:
Unsupervised Learning for Expert-Based Software Quality Estimation. HASE 2004: 149-155
[c72]Taghi M. Khoshgoftaar, Mohamed E. Abushadi:
Resource-Sensitive Intrusion Detection Models for Network Traffic. HASE 2004: 249-258
[c71]Naeem Seliya, Taghi M. Khoshgoftaar, Shi Zhong:
Semi-Supervised Learning for Software Quality Estimation. ICTAI 2004: 183-190
[c70]Qiming Luo, Taghi M. Khoshgoftaar:
Efficient Image Segmentation by Mean Shift Clustering and MDL-Guided Region Merging. ICTAI 2004: 337-343
[c69]Wei Tang, Taghi M. Khoshgoftaar:
Noise Identification with the k-Means Algorithm. ICTAI 2004: 373-378
[c68]Taghi M. Khoshgoftaar, Naeem Seliya, Kehan Gao:
Rule-Based Noise Detection for Software Measurement Data. IRI 2004: 302-307
[c67]Taghi M. Khoshgoftaar, Pierre Rebours:
Generating Multiple Noise Elimination Filters with the Ensemble-Partitioning Filter. IRI 2004: 369-375
[c66]Taghi M. Khoshgoftaar, Naeem Seliya:
The Necessity of Assuring Quality in Software Measurement Data. IEEE METRICS 2004: 119-130
[c65]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Module-Order Modeling using an Evolutionary Multi-Objective Optimization Approach. IEEE METRICS 2004: 159-169
[c64]Taghi M. Khoshgoftaar, Yudong Xiao, Kehan Gao:
Multi-Objective Optimization by CBR GA-Optimizer for Module-Order Modeling. SEKE 2004: 220-225
[c63]Taghi M. Khoshgoftaar, Vedang H. Joshi:
Noise Elimination with Ensemble-Classifier Filtering: A Case-Study in Software Quality Engineerin. SEKE 2004: 226-231- 2003
[j53]Taghi M. Khoshgoftaar, Naeem Seliya:
Fault Prediction Modeling for Software Quality Estimation: Comparing Commonly Used Techniques. Empir. Softw. Eng. 8(3): 255-283 (2003)
[j52]Taghi M. Khoshgoftaar, Naeem Seliya:
Analogy-Based Practical Classification Rules for Software Quality Estimation. Empir. Softw. Eng. 8(4): 325-350 (2003)
[j51]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using the SPRINT Decision Tree Algorithm. Int. J. Artif. Intell. Tools 12(3): 207-225 (2003)
[j50]Zhiwei Xu
, Taghi M. Khoshgoftaar, Edward B. Allen:
Application of fuzzy expert systems in assessing operational risk of software. Inf. Softw. Technol. 45(7): 373-388 (2003)
[j49]Taghi M. Khoshgoftaar, Edward B. Allen:
Ordering Fault-Prone Software Modules. Softw. Qual. J. 11(1): 19-37 (2003)
[j48]Taghi M. Khoshgoftaar:
Introduction to the Special Issue on Quality Engineering with Computational Intelligence. Softw. Qual. J. 11(2): 85-86 (2003)
[c62]Yi Liu, Taghi M. Khoshgoftaar:
Building Decision Tree Software Quality Classification Models Using Genetic Programming. GECCO 2003: 1808-1809
[c61]Taghi M. Khoshgoftaar, Lofton A. Bullard, Kehan Gao:
Detecting Outliers Using Rule-Based Modeling for Improving CBR-Based Software Quality Classification Models. ICCBR 2003: 216-230
[c60]Taghi M. Khoshgoftaar, Laurent A. Nguyen, Kehan Gao, Jayanth Rajeevalochanam:
Application of an Attribute Selection Method to CBR-Based Software Quality Classification. ICTAI 2003: 47-52
[c59]Taghi M. Khoshgoftaar, Yi Liu, Naeem Seliya:
Genetic Programming-Based Decision Trees for Software Quality Classification. ICTAI 2003: 374-383
[c58]Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen:
Empirical Case Studies of Combining Software Quality Classification Models. QSIC 2003: 40-- 2002
[j47]Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Uncertain Classification of Fault-Prone Software Modules. Empir. Softw. Eng. 7(4): 295-295 (2002)
[j46]Taghi M. Khoshgoftaar, Bojan Cukic
, Naeem Seliya:
Predicting Fault-Prone Modules in Embedded Systems Using Analogy-Based Classification Models. Int. J. Softw. Eng. Knowl. Eng. 12(2): 201-221 (2002)
[j45]Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng:
Using regression trees to classify fault-prone software modules. IEEE Trans. Reliab. 51(4): 455-462 (2002)
[c57]Ali Idri, Taghi M. Khoshgoftaar, Alain Abran:
Can neural networks be easily interpreted in software cost estimation? FUZZ-IEEE 2002: 1162-1167
[c56]Taghi M. Khoshgoftaar, Erik Geleyn, Laurent A. Nguyen, Lofton A. Bullard:
Cost-Sensitive Boosting In Software Quality Modeling. HASE 2002: 51-62
[c55]Taghi M. Khoshgoftaar, Naeem Seliya:
Software Quality Classification Modeling Using The SPRINT Decision Tree Algorithm. ICTAI 2002: 365-374
[c54]Taghi M. Khoshgoftaar:
Improving Usefulness of Software Quality Classification Models Based on Boolean Discriminant Functions. ISSRE 2002: 221-230
[c53]Ali Idri, Alain Abran
, Taghi M. Khoshgoftaar:
Estimating Software Project Effort by Analogy Based on Linguistic Values. IEEE METRICS 2002: 21-
[c52]Taghi M. Khoshgoftaar, Erik Geleyn, Kehan Gao:
An Empirical Study of the Impact of Count Models Predictions on Module-Order Models. IEEE METRICS 2002: 161-172
[c51]Taghi M. Khoshgoftaar, Naeem Seliya:
Tree-Based Software Quality Estimation Models For Fault Prediction. IEEE METRICS 2002: 203-- 2001
[j44]Zhiwei Xu
, Taghi M. Khoshgoftaar:
Software Quality Prediction for High-Assurance Network Telecommunications Systems. Comput. J. 44(6): 557-568 (2001)
[j43]Taghi M. Khoshgoftaar, Edward B. Allen:
Controlling Overfitting in Classification-Tree Models of Software Quality. Empir. Softw. Eng. 6(1): 59-79 (2001)
[j42]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Cost-Benefit Analysis of Software Quality Models. Softw. Qual. J. 9(1): 9-30 (2001)
[j41]Taghi M. Khoshgoftaar, Edward B. Allen:
Empirical Assessment of a Software Metric: The Information Content of Operators. Softw. Qual. J. 9(2): 99-112 (2001)
[j40]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Data Mining of Software Development Databases. Softw. Qual. J. 9(3): 161-176 (2001)
[c50]Yi Liu, Taghi M. Khoshgoftaar:
Genetic Programming Model for Software Quality Classification. HASE 2001: 127-138
[c49]Taghi M. Khoshgoftaar, Kehan Gao, Robert M. Szabo:
An Application of Zero-Inflated Poisson Regression for Software Fault Prediction . ISSRE 2001: 66-73
[c48]Edward B. Allen, Taghi M. Khoshgoftaar, Ye Chen:
Measuring Coupling and Cohesion of Software Modules: An Information-Theory Approach. IEEE METRICS 2001: 124-134
[c47]Taghi M. Khoshgoftaar, Edward B. Allen, Jianyu Deng:
Controlling Overfitting in Software Quality Models: Experiments with Regression Trees and Classification. IEEE METRICS 2001: 190-198- 2000
[j39]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Accuracy of software quality models over multiple releases. Ann. Softw. Eng. 9: 103-116 (2000)
[j38]Taghi M. Khoshgoftaar, Xiaojing Yuan, Edward B. Allen:
Balancing Misclassification Rates in Classification-Tree Models of Software Quality. Empir. Softw. Eng. 5(4): 313-330 (2000)
[j37]K. Ganesan, Taghi M. Khoshgoftaar, Edward B. Allen:
Case-Based Software Quality Prediction. Int. J. Softw. Eng. Knowl. Eng. 10(2): 139-152 (2000)
[j36]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Classification-tree models of software-quality over multiple releases. IEEE Trans. Reliab. 49(1): 4-11 (2000)
[j35]Taghi M. Khoshgoftaar, Edward B. Allen:
A practical classification-rule for software-quality models. IEEE Trans. Reliab. 49(2): 209-216 (2000)
[c46]Zhiwei Xu
, Taghi M. Khoshgoftaar, Edward B. Allen:
Prediction of software faults using fuzzy nonlinear regression modeling. HASE 2000: 281-290
[c45]Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen:
Using product, process, and execution metrics to predict fault-prone software modules with classification trees. HASE 2000: 301-310
[c44]Taghi M. Khoshgoftaar, Edward B. Allen, Jason C. Busboom:
Modeling software quality: the Software Measurement Analysis and Reliability Toolkit. ICTAI 2000: 54-61
[c43]Taghi M. Khoshgoftaar, Ruqun Shan, Edward B. Allen:
Improving Tree-Based Models of Software Quality with Principal Components Analysis. ISSRE 2000: 198-209
[c42]Taghi M. Khoshgoftaar, Vishal Thaker, Edward B. Allen:
Modeling Fault-Prone Modules of Subsystems. ISSRE 2000: 259-269
1990 – 1999
- 1999
[j34]Taghi M. Khoshgoftaar, Edward B. Allen:
A Comparative Study of Ordering and Classification of Fault-Prone Software Modules. Empir. Softw. Eng. 4(2): 159-186 (1999)
[j33]Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones
, John P. Hudepohl:
Using Classification Trees for Software Quality Models: Lessons Learned. Int. J. Softw. Eng. Knowl. Eng. 9(2): 217-231 (1999)
[j32]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Data Mining for Predictors of Software Quality. Int. J. Softw. Eng. Knowl. Eng. 9(5): 547-563 (1999)
[j31]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones
, John P. Hudepohl:
Which Software Modules have Faults which will be Discovered by Customers? J. Softw. Maintenance Res. Pract. 11(1): 1-18 (1999)
[c41]Wendell D. Jones
, John P. Hudepohl, Taghi M. Khoshgoftaar, Edward B. Allen:
Application of a Usage Profile in Software Quality Models. CSMR 1999: 148-159
[c40]Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen:
Using Genetic Programming to Determine Software Quality. FLAIRS 1999: 113-117
[c39]Matthew P. Evett, Taghi M. Khoshgoftaar, Pei-der Chien, Edward B. Allen:
Modelling software quality with GP. GECCO 1999: 1232
[c38]Taghi M. Khoshgoftaar, Edward B. Allen:
Predicting Fault-Prone Software Modules in Embedded Systems with Classification Trees. HASE 1999: 105-114
[c37]Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl:
Experience Paper: Preparing Measurements of Legacy Software for Predicting Operational Faults. ICSM 1999: 359-
[c36]Taghi M. Khoshgoftaar, Edward B. Allen, Wendell D. Jones, John P. Hudepohl:
Classification tree models of software quality over multiple releases. ISSRE 1999: 116-125
[c35]Edward B. Allen, Taghi M. Khoshgoftaar:
Measuring Coupling and Cohesion: An Information-Theory Approach. IEEE METRICS 1999: 119-
[c34]Taghi M. Khoshgoftaar, Edward B. Allen, Xiaojing Yuan, Wendell D. Jones, John P. Hudepohl:
Assessing Uncertain Predictions of Software Quality. IEEE METRICS 1999: 159-
[c33]Norman F. Schneidewind, Wendell D. Jones, Taghi M. Khoshgoftaar, Paul W. Oman, George E. Stark:
Can Metrics and Models be Applied Across Multiple Releases or Projects? IEEE METRICS 1999: 324-- 1998
[j30]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass:
Using Process History to Predict Software Quality. Computer 31(4): 66-72 (1998)
[j29]Taghi M. Khoshgoftaar, Edward B. Allen:
Classification of Fault-Prone Software Modules: Prior Probabilities, Costs, and Model Evaluation. Empir. Softw. Eng. 3(3): 275-298 (1998)
[c32]Taghi M. Khoshgoftaar, Edward B. Allen, Archana Naik, Wendell D. Jones, John P. Hudepohl:
Using Classification Trees for Software Quality Models: Lessons Learned. HASE 1998: 82-89
[c31]Donald F. Schenker, Taghi M. Khoshgoftaar:
The Application of Fuzzy Enhanced Case-Based Reasoning for Identifying Fault-Prone Modules. HASE 1998: 90-97
[c30]Wendell D. Jones, Taghi M. Khoshgoftaar, John C. Munson, T. Troy Pearse, George E. Stark:
Hitting the Moving Target: Trials and Tribulations of Modeling Quality in Evolving Software Systems. ICSM 1998: 66-67
[c29]Taghi M. Khoshgoftaar, Edward B. Allen:
Can a Software Quality Model Hit a Moving Target? ICSM 1998: 68-70
[c28]Taghi M. Khoshgoftaar, Edward B. Allen:
Predicting the order of fault-prone modules in legacy software. ISSRE 1998: 344-353- 1997
[j28]Taghi M. Khoshgoftaar, Edward B. Allen, David L. Lanning:
An Information Theory-Based Approach to Quantifying the Contribution of a Software Metric. J. Syst. Softw. 36(2): 103-113 (1997)
[j27]Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl, Stephen J. Aud:
Application of neural networks to software quality modeling of a very large telecommunications system. IEEE Trans. Neural Networks 8(4): 902-909 (1997)
[c27]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio, Ronald M. Flass:
Process Measures for Predicting Software Quality. HASE 1997: 155-161
[c26]Robert Hochman, Taghi M. Khoshgoftaar, Edward B. Allen, John P. Hudepohl:
Evolutionary neural networks: a robust approach to software reliability problems. ISSRE 1997: 13-26
[c25]Taghi M. Khoshgoftaar, K. Ganesan, Edward B. Allen, Fletcher D. Ross, Rama Munikoti, Nishith Goel, Amit Nandi:
Predicting fault-prone modules with case-based reasoning. ISSRE 1997: 27-35
[c24]Taghi M. Khoshgoftaar, Edward B. Allen:
The Impact of Costs of Misclassification on Software Quality Modeling. IEEE METRICS 1997: 54-- 1996
[j26]Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel:
The impact of software evolution and reuse on software quality. Empir. Softw. Eng. 1(1): 31-44 (1996)
[j25]Taghi M. Khoshgoftaar, Edward B. Allen, Kalai Kalaichelvan, Nishith Goel:
Early Quality Prediction: A Case Study in Telecommunications. IEEE Softw. 13(1): 65-71 (1996)
[j24]John P. Hudepohl, Stephen J. Aud, Taghi M. Khoshgoftaar, Edward B. Allen, Jean Mayrand:
Emerald: Software Metrics and Models on the Desktop. IEEE Softw. 13(5): 56-60 (1996)
[j23]Taghi M. Khoshgoftaar, David L. Lanning:
Analysis and differentiation of software system environments. Softw. Qual. J. 5(2): 127-139 (1996)
[j22]Taghi M. Khoshgoftaar, Robert M. Szabo:
Using neural networks to predict software faults during testing. IEEE Trans. Reliab. 45(3): 456-462 (1996)
[c23]Taghi M. Khoshgoftaar, Edward B. Allen, Lofton A. Bullard, Robert Halstead, Gary P. Trio:
A tree-based classification model for analysis of a military software system. HASE 1996: 244-251
[c22]Taghi M. Khoshgoftaar, Edward B. Allen, Robert Halstead, Gary P. Trio:
Detection of Fault-Prone Software Modules During a Spiral Life Cycle. ICSM 1996: 69-76
[c21]


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