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ORCIDSotiris B. Kotsiantis
Sotiris Kotsiantis – Sotirios Kotsiantis
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- affiliation: University of Patras, Greece
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Journal Articles
- 2025
[j94]Panos K. Syriopoulos, Nektarios G. Kalampalikis, Sotiris B. Kotsiantis, Michael N. Vrahatis:
kNN Classification: a review. Ann. Math. Artif. Intell. 93(1): 43-75 (2025)- [j93]Vasilis Papastefanopoulos
, Pantelis Linardatos, Sotiris Kotsiantis:
Combining normalizing flows with decision trees for interpretable unsupervised outlier detection. Eng. Appl. Artif. Intell. 141: 109770 (2025) - [j92]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Enhancing sentiment analysis with distributional emotion embeddings. Neurocomputing 634: 129822 (2025) - 2024
- [j91]Andreas F. Gkontzis, Sotiris Kotsiantis, Georgios Feretzakis, Vassilios S. Verykios:
Temporal Dynamics of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis. Big Data Cogn. Comput. 8(3): 27 (2024) - [j90]Andreas F. Gkontzis, Sotiris Kotsiantis, Georgios Feretzakis, Vassilios S. Verykios:
Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level. Future Internet 16(2): 47 (2024) - [j89]Aristidis G. Vrahatis, Konstantinos Lazaros, Sotiris Kotsiantis:
Graph Attention Networks: A Comprehensive Review of Methods and Applications. Future Internet 16(9): 318 (2024) - [j88]Konstantinos Lazaros, Dimitris E. Koumadorakis, Aristidis G. Vrahatis, Sotiris Kotsiantis:
A comprehensive review on zero-shot-learning techniques. Intell. Decis. Technol. 18(2): 1001-1028 (2024) - [j87]Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis:
Regressor cascading for time series forecasting. Intell. Decis. Technol. 18(2): 1139-1156 (2024) - [j86]Anastasia-Dimitra Lipitakis, George A. Gravvanis, Christos K. Filelis-Papadopoulos, Sotiris Kotsiantis, Dimosthenis Anagnostopoulos:
Sparse Approximate Pseudoinverse Preconditioning for Sparse Supervised Learning Problems with More Features than Samples. Int. J. Artif. Intell. Tools 33(4): 2450011:1-2450011:22 (2024) - [j85]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Emotion-Driven Energy Load Forecasting: An Ensemble Leveraging Insights from News. Int. J. Artif. Intell. Tools 33(5): 2450013:1-2450013:19 (2024) - [j84]Fa Zhu, Xiaochun Cheng, Sotiris Kotsiantis, Ioannis Hatzilygeroudis:
Editorial: Special Issue on Heterogeneous Domain Adaptation Learning and its Applications in Intelligent System. Int. J. Artif. Intell. Tools 33(7): 2402002:1-2402002:3 (2024) - [j83]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Data-efficient software defect prediction: A comparative analysis of active learning-enhanced models and voting ensembles. Inf. Sci. 676: 120786 (2024) - 2023
- [j82]Emmanuel Pintelas, Ioannis E. Livieris, Sotiris Kotsiantis, Panayiotis E. Pintelas:
A multi-view-CNN framework for deep representation learning in image classification. Comput. Vis. Image Underst. 232: 103687 (2023) - [j81]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Investigating Deep Stock Market Forecasting with Sentiment Analysis. Entropy 25(2): 219 (2023) - [j80]Aikaterini Karanikola, Gregory Davrazos, Charalampos M. Liapis, Sotiris Kotsiantis:
Financial sentiment analysis: Classic methods vs. deep learning models. Intell. Decis. Technol. 17(4): 893-915 (2023) - [j79]Athanasios I. Salamanis, George A. Gravvanis, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Novel Sparse Feature Regression Method for Traffic Forecasting. Int. J. Artif. Intell. Tools 32(1): 2350008:1-2350008:27 (2023) - [j78]Charalampos M. Liapis, Sotiris Kotsiantis:
Temporal Convolutional Networks and BERT-Based Multi-Label Emotion Analysis for Financial Forecasting. Inf. 14(11): 596 (2023) - [j77]Athanasios I. Salamanis, George A. Gravvanis, Sotiris Kotsiantis, Konstantinos M. Giannoutakis:
A generic sparse regression imputation method for time series and tabular data. Knowl. Based Syst. 279: 110965 (2023) - [j76]Stamatis Karlos, Christos K. Aridas, Vasileios G. Kanas, Sotiris Kotsiantis:
Classification of acoustical signals by combining active learning strategies with semi-supervised learning schemes. Neural Comput. Appl. 35(1): 3-20 (2023) - [j75]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
A multivariate ensemble learning method for medium-term energy forecasting. Neural Comput. Appl. 35(29): 21479-21497 (2023) - [j74]Panagiotis E. Pintelas, Sotiris Kotsiantis, Ioannis E. Livieris:
Special Issue on Machine Learning and AI for Sensors. Sensors 23(5): 2770 (2023) - 2022
- [j73]Nikolaos S. Alachiotis, Sotiris Kotsiantis, Evangelos Sakkopoulos, Vassilios S. Verykios:
Supervised machine learning models for student performance prediction. Intell. Decis. Technol. 16(1): 93-106 (2022) - [j72]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, George A. Gravvanis, Michael N. Vrahatis:
Rotation forest of random subspace models. Intell. Decis. Technol. 16(2): 315-324 (2022) - [j71]Andreas F. Gkontzis, Sotiris Kotsiantis, Christos T. Panagiotakopoulos, Vassilios S. Verykios:
A predictive analytics framework as a countermeasure for attrition of students. Interact. Learn. Environ. 30(3): 568-582 (2022) - [j70]Andreas F. Gkontzis, Sotiris Kotsiantis, Christos T. Panagiotakopoulos, Vassilios S. Verykios:
A predictive analytics framework as a countermeasure for attrition of students. Interact. Learn. Environ. 30(6): 1028-1043 (2022) - [j69]Georgia Garani, Dionysios Papadatos, Sotiris Kotsiantis, Vassilios S. Verykios:
Meteorological Data Warehousing and Analysis for Supporting Air Navigation. Informatics 9(4): 78 (2022) - 2021
- [j68]Georgios Kostopoulos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Christos Pierrakeas, Achilles Kameas:
Interpretable Models for Early Prediction of Certification in MOOCs: A Case Study on a MOOC for Smart City Professionals. IEEE Access 9: 165881-165891 (2021) - [j67]Pantelis Linardatos, Vasilis Papastefanopoulos, Sotiris Kotsiantis:
Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy 23(1): 18 (2021) - [j66]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
A Multi-Method Survey on the Use of Sentiment Analysis in Multivariate Financial Time Series Forecasting. Entropy 23(12): 1603 (2021) - [j65]Athanasios I. Salamanis, Anastasia-Dimitra Lipitakis, George A. Gravvanis, Sotiris Kotsiantis, Dimosthenis Anagnostopoulos:
An adaptive cluster-based sparse autoregressive model for large-scale multi-step traffic forecasting. Expert Syst. Appl. 180: 115093 (2021) - [j64]Aikaterini Karanikola, Charalampos M. Liapis, Sotiris Kotsiantis:
Investigating cluster validation metrics for optimal number of clusters determination. Intell. Decis. Technol. 15(4): 809-824 (2021) - [j63]Maria Tsiakmaki, Georgios Kostopoulos, Sotiris Kotsiantis, Omiros Ragos:
Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach. J. Comput. High. Educ. 33(3): 635-667 (2021) - [j62]Emmanuel Pintelas, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, Panagiotis E. Pintelas:
A novel explainable image classification framework: case study on skin cancer and plant disease prediction. Neural Comput. Appl. 33(22): 15171-15189 (2021) - [j61]Gautam Srivastava, Jerry Chun-Wei Lin, Dragan Pamucar, Sotiris Kotsiantis:
Editorial: Applications of Fuzzy Systems in Data Science and Big Data. IEEE Trans. Fuzzy Syst. 29(1): 1-3 (2021) - 2020
- [j60]Christos K. Aridas, Stamatis Karlos, Vasileios G. Kanas, Nikos Fazakis, Sotiris B. Kotsiantis:
Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets. IEEE Access 8: 2122-2133 (2020) - [j59]Nikos Fazakis, Georgios Kostopoulos, Sotiris Kotsiantis, Iosif Mporas:
Iterative Robust Semi-Supervised Missing Data Imputation. IEEE Access 8: 90555-90569 (2020) - [j58]Stamatis Karlos, Georgios Kostopoulos, Sotiris Kotsiantis:
A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems. Algorithms 13(1): 26 (2020) - [j57]Konstantinos Lavidas, Anthi Achriani, Stavros Athanassopoulos, Ioannis Messinis, Sotiris Kotsiantis:
University students' intention to use search engines for research purposes: A structural equation modeling approach. Educ. Inf. Technol. 25(4): 2463-2479 (2020) - [j56]Stefania Tomasiello, Feng Feng, Sotiris Kotsiantis, Alireza Khastan:
Special issue on "Intelligent and fuzzy systems in data science and big data". Evol. Intell. 13(2): 131 (2020) - [j55]Nikos Fazakis, Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
An active learning ensemble method for regression tasks. Intell. Data Anal. 24(3): 607-623 (2020) - [j54]Andreas F. Gkontzis, Sotiris Kotsiantis, Dimitris Kalles, Christos T. Panagiotakopoulos, Vassilios S. Verykios:
Polarity, emotions and online activity of students and tutors as features in predicting grades. Intell. Decis. Technol. 14(3): 409-436 (2020) - [j53]Vangjel Kazllarof, Stamatis Karlos, Sotiris Kotsiantis:
Investigation of Combining Logitboost(M5P) under Active Learning Classification Tasks. Informatics 7(4): 50 (2020) - [j52]Emmanuel Pintelas, Meletis Liaskos, Ioannis E. Livieris, Sotiris Kotsiantis, Panayiotis E. Pintelas:
Explainable Machine Learning Framework for Image Classification Problems: Case Study on Glioma Cancer Prediction. J. Imaging 6(6): 37 (2020) - 2019
- [j51]Vangjel Kazllarof, Stamatis Karlos, Sotiris Kotsiantis:
Active learning Rotation Forest for multiclass classification. Comput. Intell. 35(4): 891-918 (2019) - [j50]Nikos Fazakis, Vasileios G. Kanas, Christos K. Aridas, Stamatis Karlos, Sotiris Kotsiantis:
Combination of Active Learning and Semi-Supervised Learning under a Self-Training Scheme. Entropy 21(10): 988 (2019) - [j49]Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Hybrid local boosting utilizing unlabeled data in classification tasks. Evol. Syst. 10(1): 51-61 (2019) - [j48]Georgios Kostopoulos, Sotiris Kotsiantis, Nikos Fazakis, Giannis Koutsonikos, Christos Pierrakeas:
A Semi-Supervised Regression Algorithm for Grade Prediction of Students in Distance Learning Courses. Int. J. Artif. Intell. Tools 28(4): 1940001:1-1940001:19 (2019) - [j47]Stamatios-Aggelos N. Alexandropoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Data preprocessing in predictive data mining. Knowl. Eng. Rev. 34: e1 (2019) - [j46]Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
A multi-scheme semi-supervised regression approach. Pattern Recognit. Lett. 125: 758-765 (2019) - [j45]Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis:
Multiview Learning for Early Prognosis of Academic Performance: A Case Study. IEEE Trans. Learn. Technol. 12(2): 212-224 (2019) - 2018
- [j44]George Kostopoulos, Sotiris Kotsiantis, Christos Pierrakeas, Giannis Koutsonikos, George A. Gravvanis:
Forecasting students' success in an open university. Int. J. Learn. Technol. 13(1): 26-43 (2018) - [j43]Georgios Kostopoulos, Ioannis E. Livieris, Sotiris B. Kotsiantis, Vassilis Tampakas:
CST-Voting: A semi-supervised ensemble method for classification problems. J. Intell. Fuzzy Syst. 35(1): 99-109 (2018) - [j42]Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Omiros Ragos:
Semi-supervised regression: A recent review. J. Intell. Fuzzy Syst. 35(2): 1483-1500 (2018) - 2017
- [j41]Stamatis Karlos, Nikos Fazakis, Angeliki-Panagiota Panagopoulou, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Locally application of naive Bayes for self-training. Evol. Syst. 8(1): 3-18 (2017) - [j40]Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Self-Trained Stacking Model for Semi-Supervised Learning. Int. J. Artif. Intell. Tools 26(2): 1750001:1-1750001:21 (2017) - [j39]Sotiris Kotsiantis, Nikolaos K. Tselios, Michalis Xenos:
Students' evaluation of tutors in distance education: a quasi-longitudinal study. Int. J. Learn. Technol. 12(1): 26-41 (2017) - [j38]Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Self-trained Rotation Forest for semi-supervised learning. J. Intell. Fuzzy Syst. 32(1): 711-722 (2017) - 2016
- [j37]Stamatis Karlos, Nikos Fazakis, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas:
A Semisupervised Cascade Classification Algorithm. Appl. Comput. Intell. Soft Comput. 2016: 5919717:1-5919717:14 (2016) - [j36]Nikos Fazakis, Stamatis Karlos, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas:
Self-Trained LMT for Semisupervised Learning. Comput. Intell. Neurosci. 2016: 3057481:1-3057481:13 (2016) - 2014
- [j35]Sotiris B. Kotsiantis:
Integrating global and local application of naive bayes classifier. Int. Arab J. Inf. Technol. 11(3): 300-307 (2014) - [j34]Sotiris B. Kotsiantis:
A hybrid decision tree classifier. J. Intell. Fuzzy Syst. 26(1): 327-336 (2014) - [j33]Sotiris B. Kotsiantis:
Integrating global and local application of random subspace ensemble. J. Intell. Fuzzy Syst. 26(2): 731-739 (2014) - [j32]Sotiris B. Kotsiantis:
Bagging and boosting variants for handling classifications problems: a survey. Knowl. Eng. Rev. 29(1): 78-100 (2014) - 2013
- [j31]Sotiris B. Kotsiantis:
Decision trees: a recent overview. Artif. Intell. Rev. 39(4): 261-283 (2013) - 2012
- [j30]Sotiris B. Kotsiantis:
Use of machine learning techniques for educational proposes: a decision support system for forecasting students' grades. Artif. Intell. Rev. 37(4): 331-344 (2012) - [j29]Sotiris B. Kotsiantis:
Integrating Global and Local Voting of Classifiers. Cybern. Syst. 43(5): 398-409 (2012) - [j28]Dimitris Kanellopoulos, Sotiris Kotsiantis:
Evaluating and recommending Greek newspapers' websites using clustering. Program 46(1): 71-91 (2012) - 2011
- [j27]Sotiris B. Kotsiantis:
Combining bagging, boosting, rotation forest and random subspace methods. Artif. Intell. Rev. 35(3): 223-240 (2011) - [j26]Sotiris B. Kotsiantis:
An incremental ensemble of classifiers. Artif. Intell. Rev. 36(4): 249-266 (2011) - [j25]Sotiris B. Kotsiantis:
Cascade Generalization with Reweighting Data for Handling Imbalanced Problems. Comput. J. 54(9): 1547-1559 (2011) - [j24]Sotiris B. Kotsiantis:
A random subspace method that uses different instead of similar models for regression and classification problems. Int. J. Inf. Decis. Sci. 3(2): 173-188 (2011) - [j23]Dimitris Kanellopoulos, Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Intelligent Systems and Knowledge Management (Part II). J. Comput. Methods Sci. Eng. 11(3): 89-90 (2011) - 2010
- [j22]Sotiris B. Kotsiantis, Dimitris Kanellopoulos:
Cascade generalisation for ordinal problems. Int. J. Artif. Intell. Soft Comput. 2(1/2): 46-57 (2010) - [j21]Sotiris B. Kotsiantis, Dimitris N. Kanellopoulos:
Bagging different instead of similar models for regression and classification problems. Int. J. Comput. Appl. Technol. 37(1): 20-28 (2010) - [j20]Sotiris B. Kotsiantis:
Rotation-based model trees for classification. Int. J. Data Anal. Tech. Strateg. 2(1): 22-37 (2010) - [j19]Sotiris B. Kotsiantis:
Local rotation-based ensemble. Int. J. Knowl. Eng. Data Min. 1(2): 147-160 (2010) - [j18]Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Vasilis Tampakas:
Financial Application of Multi-Instance Learning: Two Greek Case Studies. J. Convergence Inf. Technol. 5(8): 42-53 (2010) - [j17]Sotiris B. Kotsiantis, Kiriakos Patriarcheas, Michalis Nik Xenos:
A combinational incremental ensemble of classifiers as a technique for predicting students' performance in distance education. Knowl. Based Syst. 23(6): 529-535 (2010) - 2009
- [j16]Sotiris B. Kotsiantis:
Locally application of random subspace with simple Bayesian classifier. Int. J. Data Min. Model. Manag. 1(4): 375-392 (2009) - [j15]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Selective costing ensemble for handling imbalanced data sets. Int. J. Hybrid Intell. Syst. 6(3): 123-133 (2009) - [j14]Sotiris B. Kotsiantis:
Educational data mining: a case study for predicting dropout-prone students. Int. J. Knowl. Eng. Soft Data Paradigms 1(2): 101-111 (2009) - 2008
- [j13]Sotiris B. Kotsiantis:
Locally application of cascade generalization for classification problems. Intell. Decis. Technol. 2(4): 239-246 (2008) - [j12]Sotiris B. Kotsiantis:
Local reweight wrapper for the problem of imbalance. Int. J. Artif. Intell. Soft Comput. 1(1): 25-38 (2008) - [j11]Sotiris B. Kotsiantis:
Handling imbalanced data sets with a modification of Decorate algorithm. Int. J. Comput. Appl. Technol. 33(2/3): 91-98 (2008) - 2007
- [j10]Sotiris B. Kotsiantis:
Credit risk analysis using a hybrid data mining model. Int. J. Intell. Syst. Technol. Appl. 2(4): 345-356 (2007) - [j9]Sotiris B. Kotsiantis:
Supervised Machine Learning: A Review of Classification Techniques. Informatica (Slovenia) 31(3): 249-268 (2007) - [j8]Sotiris B. Kotsiantis, Dimitris Tzelepis, Euaggelos Koumanakos, Vasilis Tampakas:
Selective costing voting for bankruptcy prediction. Int. J. Knowl. Based Intell. Eng. Syst. 11(2): 115-127 (2007) - 2006
- [j7]Sotiris B. Kotsiantis, Ioannis D. Zaharakis, Panayiotis E. Pintelas:
Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3): 159-190 (2006) - [j6]Sotiris B. Kotsiantis:
Local averaging of heterogeneous regression models. Int. J. Hybrid Intell. Syst. 3(2): 99-107 (2006) - [j5]Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Panayiotis E. Pintelas:
Local Boosting of Decision Stumps for Regression and Classification Problems. J. Comput. 1(4): 30-37 (2006) - 2005
- [j4]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Logitboost of Simple Bayesian Classifier. Informatica (Slovenia) 29(1): 53-60 (2005) - [j3]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Local voting of weak classifiers. Int. J. Knowl. Based Intell. Eng. Syst. 9(3): 239-248 (2005) - 2004
- [j2]Sotiris B. Kotsiantis, Christos Pierrakeas, Panayiotis E. Pintelas:
Predicting Students' Performance In Distance Learning Using Machine Learning Techniques. Appl. Artif. Intell. 18(5): 411-426 (2004) - [j1]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
A decision support prototype tool for predicting student performance in an ODL environment. Interact. Technol. Smart Educ. 1(4): 253-264 (2004)
Conference and Workshop Papers
- 2024
- [c92]Vassilios S. Verykios, Evgenia Paxinou, Aris Gkoulalas-Divanis, Manolis Tzagarakis, Sotirios Kotsiantis, Georgios Feretzakis, Dimitris Kalles:
The Faculty Assignment Problem in Higher Education: A Shapley Value-Based Approach. AIAI (4) 2024: 224-237 - [c91]Gregory Davrazos, George Raftopoulos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
Enhancing Occupancy Detection Through IoT: A Comparative Analysis of Classifiers. IISA 2024: 1-4 - [c90]Gregory Davrazos, George Raftopoulos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
Enhancing Predictive Maintenance with Interpretable AutoML: A Case Study on Detecting Ball-Bearing Faults Using IoT Data. IISA 2024: 1-4 - [c89]Gregory Davrazos, George Raftopoulos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
Predictive Vigilance: Harnessing Internet of Things and Machine Learning for Smoke Detection. IISA 2024: 1-4 - [c88]Gregory Davrazos, George Raftopoulos, Georgios Kostopoulos, Sotiris Kotsiantis:
Optimizing Service Efficiency: Incoming Call Forecasting Utilizing Smart City Open Data - A Case Study. SETN 2024: 36:1-36:4 - 2023
- [c87]Vangjel Kazllarof, Sotiris Kotsiantis:
Active Learning Query Strategy Selection Using Dataset Meta-features Extraction. AIAI (2) 2023: 185-194 - [c86]Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis:
Water Quality Estimation from IoT Sensors Using a Meta-ensemble. AIAI (Workshops) 2023: 393-403 - [c85]Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
Predicting Cost of Municipal Waste Management using IoT Data and Machine Learning. IISA 2023: 1-4 - [c84]Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
Android Malware Detection in IoT Mobile Devices using a Meta-ensemble Classifier. IISA 2023: 1-4 - [c83]Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
IoT Device Identification Using a Meta-Ensemble Multi-Class Classifier. IISA 2023: 1-4 - [c82]Gregory Davrazos, Theodor Panagiotakopoulos, Sotiris Kotsiantis, Achilles Kameas:
IoT-Enabled Crop Recommendation in Smart Agriculture Using Machine Learning. IISA 2023: 1-4 - 2022
- [c81]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
Energy Load Forecasting: Investigating Mid-Term Predictions with Ensemble Learners. AIAI (1) 2022: 343-355 - [c80]Panos K. Syriopoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Survey on KNN Methods in Data Science. LION 2022: 379-393 - [c79]Charalampos M. Liapis, Sotiris Kotsiantis:
Energy Balance Forecasting: An Extensive Multivariate Regression Models Comparison. SETN 2022: 41:1-41:7 - [c78]Vangjel Kazllarof, Sotiris Kotsiantis:
Human Activity Recognition using Time Series Feature Extraction and Active Learning. SETN 2022: 46:1-46:4 - 2021
- [c77]Vangjel Kazllarof, Sotiris Kotsiantis:
Active Bagging Ensemble Selection. AIAI (Workshops) 2021: 455-465 - [c76]Theodor Panagiotakopoulos, Sotiris Kotsiantis, Spiros A. Borotis, Fotis Lazarinis, Achilles Kameas:
Applying Machine Learning to Predict Whether Learners Will Start a MOOC After Initial Registration. AIAI (Workshops) 2021: 466-475 - [c75]Aikaterini Karanikola, Charalampos M. Liapis, Sotiris Kotsiantis:
A comparative study of validity indices on estimating the optimal number of clusters. IISA 2021: 1-8 - [c74]Iliana Paliari, Aikaterini Karanikola, Sotiris Kotsiantis:
A comparison of the optimized LSTM, XGBOOST and ARIMA in Time Series forecasting. IISA 2021: 1-7 - 2020
- [c73]Vangjel Kazllarof, Sotiris B. Kotsiantis:
Active Hidden Naive Bayes. PCI 2020: 38-41 - [c72]Charalampos M. Liapis, Aikaterini Karanikola, Sotiris Kotsiantis:
An ensemble forecasting method using univariate time series COVID-19 data. PCI 2020: 50-52 - 2019
- [c71]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis:
A Deep Dense Neural Network for Bankruptcy Prediction. EANN 2019: 435-444 - [c70]Stamatis Karlos, Vasileios G. Kanas, Nikos Fazakis, Christos K. Aridas, Sotiris Kotsiantis:
Investigating the Benefits of Exploiting Incremental Learners Under Active Learning Scheme. AIAI 2019: 37-49 - [c69]Stamatios-Aggelos N. Alexandropoulos, Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Stacking Strong Ensembles of Classifiers. AIAI 2019: 545-556 - [c68]Nikos Fazakis, Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Self-trained eXtreme Gradient Boosting Trees. IISA 2019: 1-6 - [c67]Stamatis Karlos, Vasileios G. Kanas, Christos K. Aridas, Nikos Fazakis, Sotiris Kotsiantis:
Combining Active Learning with Self-train algorithm for classification of multimodal problems. IISA 2019: 1-8 - [c66]Georgios Kostopoulos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Multi-objective Optimization of C4.5 Decision Tree for Predicting Student Academic Performance. IISA 2019: 1-4 - [c65]Georgios S. Temponeras, Stamatios-Aggelos N. Alexandropoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Financial Fraudulent Statements Detection through a Deep Dense Artificial Neural Network. IISA 2019: 1-5 - [c64]Dimitris G. Tsarmpopoulos, Athanasia N. Papanikolaou, Sotiris Kotsiantis, Theodoula N. Grapsa, George S. Androulakis:
Performance Evaluation and Comparison of Multi-objective optimization Algorithms. IISA 2019: 1-6 - [c63]Aikaterini Karanikola, Sotiris Kotsiantis:
A hybrid method for missing value imputation. PCI 2019: 74-79 - 2018
- [c62]Aikaterini Karanikola, Stamatis Karlos, Vangjel Kazllarof, Eirini Kateri, Sotiris Kotsiantis:
Active fuzzy rule induction. EAIS 2018: 1-8 - [c61]Stamatis Karlos, Nikos Fazakis, Konstantinos Kaleris, Vasileios G. Kanas, Sotiris Kotsiantis:
An incremental self-trained ensemble algorithm. EAIS 2018: 1-8 - [c60]Andreas F. Gkontzis, Chris T. Panagiotakopoulos, Sotiris Kotsiantis, Vassilios S. Verykios:
Measuring Engagement to Assess Performance of Students in Distance Learning. IISA 2018: 1-7 - [c59]Maria Tsiakmaki, Georgios Kostopoulos, Giannis Koutsonikos, Christos Pierrakeas, Sotiris Kotsiantis, Omiros Ragos:
Predicting University Students' Grades Based on Previous Academic Achievements. IISA 2018: 1-6 - [c58]Andreas F. Gkontzis, Sotiris Kotsiantis, Rozita Tsoni, Vassilios S. Verykios:
An effective LA approach to predict student achievement. PCI 2018: 76-81 - [c57]Aikaterini Karanikola, Stamatis Karlos, Vangjel Kazllarof, Sotiris Kotsiantis:
An incrementally updateable ensemble learner. PCI 2018: 243-248 - [c56]Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
A Semi-supervised regressor based on model trees. SETN 2018: 6:1-6:7 - [c55]Stamatis Karlos, Aikaterini Karanikola, Vangjel Kazllarof, Sotiris Kotsiantis:
Local weighted Averaged 2-Dependence Estimator. SETN 2018: 28:1-28:4 - [c54]Stamatis Karlos, Konstantinos Kaleris, Nikos Fazakis, Vasileios G. Kanas, Sotiris Kotsiantis:
Optimized Active Learning Strategy for Audiovisual Speaker Recognition. SPECOM 2018: 281-290 - 2017
- [c53]Georgios Kostopoulos, Stamatis Karlos, Sotiris Kotsiantis, Vassilis Tampakas:
Evaluating Active Learning Methods for Bankruptcy Prediction. BFAL 2017: 57-66 - [c52]Georgios Kostopoulos, Sotiris Kotsiantis, Vassilios S. Verykios:
A Prognosis of Junior High School Students' Performance Based on Active Learning Methods. BFAL 2017: 67-76 - [c51]Georgios Kostopoulos, Anastasia-Dimitra Lipitakis, Sotiris Kotsiantis, George A. Gravvanis:
Predicting Student Performance in Distance Higher Education Using Active Learning. EANN 2017: 75-86 - [c50]Christos K. Aridas, Stamatios-Aggelos N. Alexandropoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems. EANN 2017: 111-121 - [c49]Stamatis Karlos, Georgios Kostopoulos, Sotiris Kotsiantis, Vassilis Tampakas:
Using Active Learning Methods for Predicting Fraudulent Financial Statements. EANN 2017: 351-362 - [c48]Georgios Kostopoulos, Sotiris Kotsiantis, Omiros Ragos, Theodoula N. Grapsa:
Early dropout prediction in distance higher education using active learning. IISA 2017: 1-6 - [c47]Georgios Kostopoulos, Ioannis E. Livieris, Sotiris Kotsiantis, Vassilis Tampakas:
Enhancing high school students' performance based on semi-supervised methods. IISA 2017: 1-6 - [c46]Vangjel Kazllarof, Stamatis Karlos, Sotiris Kotsiantis, Michalis Xenos:
Automated hand gesture recognition exploiting Active Learning methods. PCI 2017: 3:1-3:6 - 2016
- [c45]Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Increasing Diversity in Random Forests Using Naive Bayes. AIAI 2016: 75-86 - [c44]Christos K. Aridas, Sotiris B. Kotsiantis, Michael N. Vrahatis:
Combining Prototype Selection with Local Boosting. AIAI 2016: 94-105 - [c43]Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Self-labeled Hidden Naive Bayes algorithm for semi-supervised classification. IISA 2016: 1-6 - [c42]Stamatis Karlos, Sotiris Kotsiantis, Nikos Fazakis, Kyriakos N. Sgarbas:
Effectiveness of semi-supervised learning in bankruptcy prediction. IISA 2016: 1-6 - [c41]Vangjel Kazllarof, Stamatis Karlos, Angeliki-Panagiota Panagopoulou, Sotiris Kotsiantis:
Automated hand gesture recognition for educational applications. PCI 2016: 20 - [c40]Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos N. Sgarbas:
Semi-supervised forecasting of fraudulent financial statements. PCI 2016: 34 - [c39]Stamatis Karlos, Nikos Fazakis, Katerina Karanikola, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas:
Speech Recognition Combining MFCCs and Image Features. SPECOM 2016: 651-658 - 2015
- [c38]Stamatis Karlos, Nikos Fazakis, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas:
Self-Train LogitBoost for Semi-supervised Learning. EANN 2015: 139-148 - [c37]Anastasia-Dimitra Lipitakis, Gerasimos S. Antzoulatos, Sotiris Kotsiantis, Michael N. Vrahatis:
Integrating global and local boosting. IISA 2015: 1-6 - [c36]Anastasia-Dimitra Lipitakis, Sotiris Kotsiantis:
Combining ensembles algorithms of symbolic learners. IISA 2015: 1-6 - [c35]Georgios Kostopoulos, Sotiris B. Kotsiantis, Panagiotis E. Pintelas:
Predicting Student Performance in Distance Higher Education Using Semi-supervised Techniques. MEDI 2015: 259-270 - [c34]Georgios Kostopoulos, Sotiris B. Kotsiantis, Panagiotis E. Pintelas:
Estimating student dropout in distance higher education using semi-supervised techniques. Panhellenic Conference on Informatics 2015: 38-43 - [c33]Christos K. Aridas, Sotiris B. Kotsiantis:
Combining random forest and support vector machines for semi-supervised learning. Panhellenic Conference on Informatics 2015: 123-128 - [c32]Nikos Fazakis, Stamatis Karlos, Sotiris B. Kotsiantis, Kyriakos N. Sgarbas:
Speaker Identification Using Semi-supervised Learning. SPECOM 2015: 389-396 - 2014
- [c31]Anastasia-Dimitra Lipitakis, Sotirios Kotsiantis:
A hybrid Machine Learning methodology for imbalanced datasets. IISA 2014: 252-257 - 2012
- [c30]Emmanuel Pappas, Sotiris B. Kotsiantis:
Integrating Global and Local Application of Discriminative Multinomial Bayesian Classifier for Text Classification. ISI 2012: 49-55 - [c29]Elias Zouboulidis, Sotiris B. Kotsiantis:
Forecasting Fraudulent Financial Statements with Committee of Cost-Sensitive Decision Tree Classifiers. SETN 2012: 57-64 - [c28]Despina Deligianni, Sotiris B. Kotsiantis:
Forecasting Corporate Bankruptcy with an Ensemble of Classifiers. SETN 2012: 65-72 - [c27]Elias Kamos, Foteini Matthaiou, Sotiris B. Kotsiantis:
Credit Rating Using a Hybrid Voting Ensemble. SETN 2012: 165-173 - 2008
- [c26]Sotiris B. Kotsiantis, Dimitris Kanellopoulos:
Applying Machine Learning Techniques for Environmental Reporting. NCM (1) 2008: 217-223 - [c25]Sotiris B. Kotsiantis:
Local Grading of Learners. Panhellenic Conference on Informatics 2008: 209-213 - [c24]Sotiris B. Kotsiantis:
Stacking Cost Sensitive Models. Panhellenic Conference on Informatics 2008: 217-221 - 2007
- [c23]Dimitris Kanellopoulos, Sotiris B. Kotsiantis, Vasilis Tampakas:
Towards an ontology-based system for intelligent prediction of firms with fraudulent financial statements. ETFA 2007: 1300-1307 - [c22]D. Anyfantis, M. Karagiannopoulos, Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Robustness of learning techniques in handling class noise in imbalanced datasets. AIAI 2007: 21-28 - [c21]M. Karagiannopoulos, D. Anyfantis, Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
A Wrapper for Reweighting Training Instances for Handling Imbalanced Data Sets. AIAI 2007: 29-36 - [c20]Sotiris B. Kotsiantis, Dimitris Kanellopoulos:
Combining Bagging, Boosting and Dagging for Classification Problems. KES (2) 2007: 493-500 - 2006
- [c19]Dimitris Kanellopoulos, Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Ontology-based Learning Applications: A Development Methodology. IASTED Conf. on Software Engineering 2006: 27-32 - [c18]Sotiris B. Kotsiantis, Euaggelos Koumanakos, Dimitris Tzelepis, Vasilis Tampakas:
Financial Application of Neural Networks: Two Case Studies in Greece. ICANN (2) 2006: 672-681 - [c17]Sotiris B. Kotsiantis:
Local Ordinal Classification. AIAI 2006: 1-8 - [c16]Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Ioannis D. Zaharakis:
Bagged Averaging of Regression Models. AIAI 2006: 53-60 - [c15]Sotiris B. Kotsiantis, Dimitris Kanellopoulos, Panayiotis E. Pintelas:
Local Additive Regression of Decision Stumps. SETN 2006: 148-157 - [c14]Sotiris B. Kotsiantis, Euaggelos Koumanakos, Dimitris Tzelepis, Vasilis Tampakas:
Predicting Fraudulent Financial Statements with Machine Learning Techniques. SETN 2006: 538-542 - 2005
- [c13]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Predicting Students' Marks in Hellenic Open University. ICALT 2005: 664-668 - [c12]Sotiris B. Kotsiantis, George E. Tsekouras, Panayiotis E. Pintelas:
Local Bagging of Decision Stumps. IEA/AIE 2005: 406-411 - [c11]George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis, Panayiotis E. Pintelas:
A Fuzzy Logic-Based Approach for Detecting Shifting Patterns in Cross-Cultural Data. IEA/AIE 2005: 705-708 - [c10]Sotiris B. Kotsiantis, George E. Tsekouras, C. Raptis, Panayiotis E. Pintelas:
Modeling the Organoleptic Properties of Matured Wine Distillates. MLDM 2005: 667-673 - [c9]Sotiris B. Kotsiantis, George E. Tsekouras, Panayiotis E. Pintelas:
Bagging Random Trees for Estimation of Tissue Softness. MLDM 2005: 674-681 - [c8]Sotiris B. Kotsiantis, George E. Tsekouras, Panayiotis E. Pintelas:
Bagging Model Trees for Classification Problems. Panhellenic Conference on Informatics 2005: 328-337 - 2004
- [c7]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Bagged Voting Ensembles. AIMSA 2004: 168-177 - [c6]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
Increasing the Classification Accuracy of Simple Bayesian Classifier. AIMSA 2004: 198-207 - [c5]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
A Hybrid Decision Support Tool - Using Ensemble of Classifiers. ICEIS (2) 2004: 448-456 - [c4]George E. Tsekouras, Dimitris Papageorgiou, Sotiris B. Kotsiantis, Christos Kalloniatis, Panayiotis E. Pintelas:
Fuzzy Clustering of Categorical Attributes and its Use in Analyzing Cultural Data. International Conference on Computational Intelligence 2004: 202-206 - [c3]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
An Online Ensemble of Classifiers. PRIS 2004: 59-68 - [c2]Sotiris B. Kotsiantis, Panayiotis E. Pintelas:
A Cost Sensitive Technique for Ordinal Classification Problems. SETN 2004: 220-229 - 2003
- [c1]Sotiris B. Kotsiantis, Christos Pierrakeas, Panayiotis E. Pintelas:
Preventing Student Dropout in Distance Learning Using Machine Learning Techniques. KES 2003: 267-274
Parts in Books or Collections
- 2020
- [p2]Christos Pierrakeas, Giannis Koutsonikos, Anastasia-Dimitra Lipitakis, Sotiris Kotsiantis, Michalis Xenos, George A. Gravvanis:
The Variability of the Reasons for Student Dropout in Distance Learning and the Prediction of Dropout-Prone Students. Machine Learning Paradigms 2020: 91-111 - 2007
- [p1]Sotiris B. Kotsiantis:
Supervised Machine Learning: A Review of Classification Techniques. Emerging Artificial Intelligence Applications in Computer Engineering 2007: 3-24
Coauthor Index
Dimitris Kanellopoulos
aka: Dimitris N. Kanellopoulos
aka: Dimitris N. Kanellopoulos
Katerina Karanikola
aka: Aikaterini Karanikola
aka: Aikaterini Karanikola
Georgios Kostopoulos
aka: George Kostopoulos
aka: George Kostopoulos
Panayiotis E. Pintelas
aka: Panagiotis E. Pintelas
aka: Panagiotis E. Pintelas
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