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2020 – today
- 2022
[j37]Felipe Bravo-Marquez
, Arun Khanchandani, Bernhard Pfahringer:
Incremental Word Vectors for Time-Evolving Sentiment Lexicon Induction. Cogn. Comput. 14(1): 425-441 (2022)- [j36]Emanuele Pio Barracchia, Gianvito Pio, Albert Bifet, Heitor Murilo Gomes, Bernhard Pfahringer, Michelangelo Ceci:
LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding. Inf. Sci. 606: 702-721 (2022) - [c131]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Predicting COVID-19 Patient Shielding: A Comprehensive Study. AI 2022: 332-343 - [c130]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification Through Self-supervision. AI 2022: 645-657 - [i25]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Balancing Performance and Energy Consumption of Bagging Ensembles for the Classification of Data Streams in Edge Computing. CoRR abs/2201.06205 (2022) - [i24]Hongyu Wang, Eibe Frank, Bernhard Pfahringer, Michael Mayo, Geoffrey Holmes:
Cross-domain Few-shot Meta-learning Using Stacking. CoRR abs/2205.05831 (2022) - 2021
- [j35]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving the performance of bagging ensembles for data streams through mini-batching. Inf. Sci. 580: 260-282 (2021) - [j34]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. J. Artif. Intell. Res. 70: 683-718 (2021) - [j33]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of neural networks by enforcing Lipschitz continuity. Mach. Learn. 110(2): 393-416 (2021) - [c129]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Transformers for Multi-label Classification of Medical Text: An Empirical Comparison. AIME 2021: 114-123 - [c128]Rajchada Chanajitt, Bernhard Pfahringer, Heitor Murilo Gomes:
Combining Static and Dynamic Analysis to Improve Machine Learning-based Malware Classification. DSAA 2021: 1-10 - [c127]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
PolyLM: Learning about Polysemy through Language Modeling. EACL 2021: 563-574 - [c126]Saulo Martiello Mastelini, Jacob Montiel, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, André C. P. L. F. de Carvalho:
Fast and lightweight binary and multi-branch Hoeffding Tree Regressors. ICDM (Workshops) 2021: 380-388 - [c125]Yunzhe Jia, Eibe Frank, Bernhard Pfahringer, Albert Bifet, Nick Lim:
Studying and Exploiting the Relationship Between Model Accuracy and Explanation Quality. ECML/PKDD (2) 2021: 699-714 - [i23]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
PolyLM: Learning about Polysemy through Language Modeling. CoRR abs/2101.10448 (2021) - [i22]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Better Self-training for Image Classification through Self-supervision. CoRR abs/2109.00778 (2021) - [i21]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image Classification. CoRR abs/2109.00788 (2021) - [i20]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-Supervised Learning using Siamese Networks. CoRR abs/2109.00794 (2021) - [i19]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Predicting COVID-19 Patient Shielding: A Comprehensive Study. CoRR abs/2110.00183 (2021) - [i18]Vithya Yogarajan, Bernhard Pfahringer, Tony Smith, Jacob Montiel:
Improving Predictions of Tail-end Labels using Concatenated BioMed-Transformers for Long Medical Documents. CoRR abs/2112.01718 (2021) - [i17]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving the performance of bagging ensembles for data streams through mini-batching. CoRR abs/2112.09834 (2021) - 2020
- [j32]Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo:
A review of Automatic end-to-end De-Identification: Is High Accuracy the Only Metric? Appl. Artif. Intell. 34(3): 251-269 (2020) - [j31]Adriano Rivolli, Jesse Read, Carlos Soares, Bernhard Pfahringer, André C. P. L. F. de Carvalho:
An empirical analysis of binary transformation strategies and base algorithms for multi-label learning. Mach. Learn. 109(8): 1509-1563 (2020) - [c124]Vithya Yogarajan, Henry Gouk, Tony Smith, Michael Mayo, Bernhard Pfahringer:
Comparing High Dimensional Word Embeddings Trained on Medical Text to Bag-of-Words for Predicting Medical Codes. ACIIDS (1) 2020: 97-108 - [c123]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Transfer of Pretrained Model Weights Substantially Improves Semi-supervised Image Classification. Australasian Conference on Artificial Intelligence 2020: 433-444 - [c122]Hongyu Wang, Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael Mayo:
A Comparison of Machine Learning Methods for Cross-Domain Few-Shot Learning. Australasian Conference on Artificial Intelligence 2020: 445-457 - [c121]Alessio Bernardo, Heitor Murilo Gomes, Jacob Montiel, Bernhard Pfahringer, Albert Bifet, Emanuele Della Valle:
C-SMOTE: Continuous Synthetic Minority Oversampling for Evolving Data Streams. IEEE BigData 2020: 483-492 - [c120]Guilherme Weigert Cassales, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer, Hermes Senger:
Improving parallel performance of ensemble learners for streaming data through data locality with mini-batching. HPCC/DSS/SmartCity 2020: 138-146 - [c119]Maroua Bahri, Bernhard Pfahringer, Albert Bifet, Silviu Maniu:
Efficient Batch-Incremental Classification Using UMAP for Evolving Data Streams. IDA 2020: 40-53 - [c118]Heitor Murilo Gomes, Jacob Montiel, Saulo Martiello Mastelini, Bernhard Pfahringer, Albert Bifet:
On Ensemble Techniques for Data Stream Regression. IJCNN 2020: 1-8 - [c117]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. IJCNN 2020: 1-8 - [c116]Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer:
confStream: Automated Algorithm Selection and Configuration of Stream Clustering Algorithms. LION 2020: 80-95 - [i16]Vithya Yogarajan, Jacob Montiel, Tony Smith, Bernhard Pfahringer:
Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes. CoRR abs/2004.00430 (2020) - [i15]Jacob Montiel, Rory Mitchell, Eibe Frank, Bernhard Pfahringer, Talel Abdessalem, Albert Bifet:
Adaptive XGBoost for Evolving Data Streams. CoRR abs/2005.07353 (2020) - [i14]Fabricio Ceschin, Heitor Murilo Gomes, Marcus Botacin, Albert Bifet, Bernhard Pfahringer, Luiz S. Oliveira, André Grégio:
Machine Learning (In) Security: A Stream of Problems. CoRR abs/2010.16045 (2020)
2010 – 2019
- 2019
- [j30]Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Merit-guided dynamic feature selection filter for data streams. Expert Syst. Appl. 116: 227-242 (2019) - [j29]Jean Paul Barddal, Fabrício Enembreck, Heitor Murilo Gomes, Albert Bifet, Bernhard Pfahringer:
Boosting decision stumps for dynamic feature selection on data streams. Inf. Syst. 83: 13-29 (2019) - [j28]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad:
AffectiveTweets: a Weka Package for Analyzing Affect in Tweets. J. Mach. Learn. Res. 20: 92:1-92:6 (2019) - [j27]Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Correction to: Adaptive random forests for evolving data stream classification. Mach. Learn. 108(10): 1877-1878 (2019) - [c115]Alex Yuxuan Peng, Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer:
Investigating the effect of novel classes in semi-supervised learning. ACML 2019: 615-630 - [c114]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. ACML 2019: 1094-1109 - [c113]Attaullah Sahito, Eibe Frank, Bernhard Pfahringer:
Semi-supervised Learning Using Siamese Networks. Australasian Conference on Artificial Intelligence 2019: 586-597 - [c112]Heitor Murilo Gomes, Rodrigo Fernandes de Mello, Bernhard Pfahringer, Albert Bifet:
Feature Scoring using Tree-Based Ensembles for Evolving Data Streams. IEEE BigData 2019: 761-769 - [c111]Jörg Wicker, Yan Cathy Hua, Rayner Rebello, Bernhard Pfahringer:
XOR-Based Boolean Matrix Decomposition. ICDM 2019: 638-647 - [c110]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On Calibration of Nested Dichotomies. PAKDD (1) 2019: 69-80 - [c109]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. PAKDD (1) 2019: 81-93 - [c108]Matthias Carnein, Heike Trautmann, Albert Bifet, Bernhard Pfahringer:
Towards Automated Configuration of Stream Clustering Algorithms. PKDD/ECML Workshops (1) 2019: 137-143 - [c107]Alan Ansell, Felipe Bravo-Marquez, Bernhard Pfahringer:
An ELMo-inspired approach to SemDeep-5's Word-in-Context task. SemDeep@IJCAI 2019: 21-25 - [i13]Henry Gouk, Bernhard Pfahringer, Eibe Frank:
Stochastic Gradient Trees. CoRR abs/1901.07777 (2019) - [i12]Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo:
Automatic end-to-end De-identification: Is high accuracy the only metric? CoRR abs/1901.10583 (2019) - [i11]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier Chains: A Review and Perspectives. CoRR abs/1912.13405 (2019) - 2018
- [j26]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
The online performance estimation framework: heterogeneous ensemble learning for data streams. Mach. Learn. 107(1): 149-176 (2018) - [j25]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Transferring sentiment knowledge between words and tweets. Web Intell. 16(4): 203-220 (2018) - [c106]Bartosz Krawczyk, Bernhard Pfahringer, Michal Wozniak:
Combining active learning with concept drift detection for data stream mining. IEEE BigData 2018: 2239-2244 - [c105]Edmond Zhang, Reece Robinson, Bernhard Pfahringer:
Deep Holistic Representation Learning from EHR. ISMICT 2018: 1-6 - [c104]Alex Yuxuan Peng, Yun Sing Koh, Patricia Riddle, Bernhard Pfahringer:
Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems. ECML/PKDD (1) 2018: 410-425 - [c103]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. ECML/PKDD (1) 2018: 541-556 - [c102]Lanqin Yuan, Bernhard Pfahringer, Jean Paul Barddal:
Iterative subset selection for feature drifting data streams. SAC 2018: 510-517 - [i10]Henry Gouk, Eibe Frank, Bernhard Pfahringer, Michael J. Cree:
Regularisation of Neural Networks by Enforcing Lipschitz Continuity. CoRR abs/1804.04368 (2018) - [i9]Henry Gouk, Bernhard Pfahringer, Eibe Frank, Michael J. Cree:
MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes. CoRR abs/1804.05965 (2018) - [i8]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. CoRR abs/1808.00111 (2018) - [i7]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
Ensembles of Nested Dichotomies with Multiple Subset Evaluation. CoRR abs/1809.02740 (2018) - [i6]Tim Leathart, Eibe Frank, Bernhard Pfahringer, Geoffrey Holmes:
On the Calibration of Nested Dichotomies for Large Multiclass Tasks. CoRR abs/1809.02744 (2018) - [i5]Vithya Yogarajan, Michael Mayo, Bernhard Pfahringer:
A survey of automatic de-identification of longitudinal clinical narratives. CoRR abs/1810.06765 (2018) - 2017
- [j24]Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer:
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions. J. Syst. Softw. 127: 278-294 (2017) - [j23]Heitor Murilo Gomes, Albert Bifet, Jesse Read, Jean Paul Barddal, Fabrício Enembreck, Bernhard Pfahringer, Geoff Holmes, Talel Abdessalem:
Adaptive random forests for evolving data stream classification. Mach. Learn. 106(9-10): 1469-1495 (2017) - [c101]Tim Leathart, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer:
Probability Calibration Trees. ACML 2017: 145-160 - [c100]Min-Hsien Weng, Bernhard Pfahringer, Mark Utting:
Static techniques for reducing memory usage in the C implementation of whiley programs. ACSW 2017: 15:1-15:8 - [c99]Paula Branco, Luís Torgo, Rita P. Ribeiro, Eibe Frank, Bernhard Pfahringer, Markus Michael Rau:
Learning Through Utility Optimization in Regression Tasks. DSAA 2017: 30-39 - [c98]Vítor Cerqueira, Luís Torgo, Mariana Oliveira, Bernhard Pfahringer:
Dynamic and Heterogeneous Ensembles for Time Series Forecasting. DSAA 2017: 242-251 - [c97]Albert Bifet, Jiajin Zhang, Wei Fan, Cheng He, Jianfeng Zhang, Jianfeng Qian, Geoff Holmes, Bernhard Pfahringer:
Extremely Fast Decision Tree Mining for Evolving Data Streams. KDD 2017: 1733-1742 - [r3]Bernhard Pfahringer:
Conjunctive Normal Form. Encyclopedia of Machine Learning and Data Mining 2017: 260-261 - [r2]Bernhard Pfahringer:
Disjunctive Normal Form. Encyclopedia of Machine Learning and Data Mining 2017: 371-372 - 2016
- [j22]Min-Hsien Weng, Mark Utting, Bernhard Pfahringer:
Bound Analysis for Whiley Programs. Electron. Notes Theor. Comput. Sci. 320: 53-67 (2016) - [j21]Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes:
MEKA: A Multi-label/Multi-target Extension to WEKA. J. Mach. Learn. Res. 17: 21:1-21:5 (2016) - [j20]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Building a Twitter opinion lexicon from automatically-annotated tweets. Knowl. Based Syst. 108: 65-78 (2016) - [c96]Henry Gouk, Bernhard Pfahringer, Michael J. Cree:
Learning Distance Metrics for Multi-Label Classification. ACML 2016: 318-333 - [c95]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis. ECAI 2016: 498-506 - [c94]Jean Paul Barddal, Heitor Murilo Gomes, Fabrício Enembreck, Bernhard Pfahringer, Albert Bifet:
On Dynamic Feature Weighting for Feature Drifting Data Streams. ECML/PKDD (2) 2016: 129-144 - [c93]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. ECML/PKDD (2) 2016: 179-194 - [c92]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Opinion Lexicons to Sentiment Classification of Tweets and Vice Versa: A Transfer Learning Approach. WI 2016: 145-152 - [c91]Felipe Bravo-Marquez, Eibe Frank, Saif M. Mohammad, Bernhard Pfahringer:
Determining Word-Emotion Associations from Tweets by Multi-label Classification. WI 2016: 536-539 - [i4]Tim Leathart, Bernhard Pfahringer, Eibe Frank:
Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection. CoRR abs/1604.01854 (2016) - 2015
- [j19]Luís Torgo, Paula Branco, Rita P. Ribeiro, Bernhard Pfahringer:
Resampling strategies for regression. Expert Syst. J. Knowl. Eng. 32(3): 465-476 (2015) - [j18]Indre Zliobaite, Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes:
Evaluation methods and decision theory for classification of streaming data with temporal dependence. Mach. Learn. 98(3): 455-482 (2015) - [c90]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Having a Blast: Meta-Learning and Heterogeneous Ensembles for Data Streams. ICDM 2015: 1003-1008 - [c89]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
Positive, Negative, or Neutral: Learning an Expanded Opinion Lexicon from Emoticon-Annotated Tweets. IJCAI 2015: 1229-1235 - [c88]Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer:
Use of ensembles of Fourier spectra in capturing recurrent concepts in data streams. IJCNN 2015: 1-8 - [c87]Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Geoff Holmes, Bernhard Pfahringer:
Efficient Online Evaluation of Big Data Stream Classifiers. KDD 2015: 59-68 - [c86]Bernhard Pfahringer:
On a Few Recent Developments in Meta-Learning for Algorithm Ranking and Selection. MetaSel@PKDD/ECML 2015: 2 - [c85]Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer:
From Unlabelled Tweets to Twitter-specific Opinion Words. SIGIR 2015: 743-746 - [e3]Bernhard Pfahringer, Jochen Renz:
AI 2015: Advances in Artificial Intelligence - 28th Australasian Joint Conference, Canberra, ACT, Australia, November 30 - December 4, 2015, Proceedings. Lecture Notes in Computer Science 9457, Springer 2015, ISBN 978-3-319-26349-6 [contents] - [i3]Sripirakas Sakthithasan, Russel Pears, Albert Bifet, Bernhard Pfahringer:
Use of Ensembles of Fourier Spectra in Capturing Recurrent Concepts in Data Streams. CoRR abs/1504.06366 (2015) - [i2]Henry Gouk, Bernhard Pfahringer, Michael J. Cree:
Learning Similarity Metrics by Factorising Adjacency Matrices. CoRR abs/1511.06442 (2015) - 2014
- [j17]Andreas Hapfelmeier, Bernhard Pfahringer, Stefan Kramer:
Pruning Incremental Linear Model Trees with Approximate Lookahead. IEEE Trans. Knowl. Data Eng. 26(8): 2072-2076 (2014) - [j16]Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, Geoffrey Holmes:
Active Learning With Drifting Streaming Data. IEEE Trans. Neural Networks Learn. Syst. 25(1): 27-39 (2014) - [c84]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Algorithm Selection on Data Streams. Discovery Science 2014: 325-336 - [c83]Jan N. van Rijn, Geoffrey Holmes, Bernhard Pfahringer, Joaquin Vanschoren:
Towards Meta-learning over Data Streams. MetaSel@ECAI 2014: 37-38 - [c82]Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet:
Détection de changements dans des flots de données qualitatives. EGC 2014: 517-520 - [c81]Dino Ienco, Indre Zliobaite, Bernhard Pfahringer:
High density-focused uncertainty sampling for active learning over evolving stream data. BigMine 2014: 133-148 - [c80]Quan Sun, Bernhard Pfahringer:
Hierarchical Meta-Rules for Scalable Meta-Learning. PRICAI 2014: 383-395 - [c79]Dino Ienco, Albert Bifet, Bernhard Pfahringer, Pascal Poncelet:
Change detection in categorical evolving data streams. SAC 2014: 792-797 - 2013
- [j15]Quan Sun, Bernhard Pfahringer:
Pairwise meta-rules for better meta-learning-based algorithm ranking. Mach. Learn. 93(1): 141-161 (2013) - [c78]Bernhard Pfahringer:
The MOA Data Stream Mining Tool: A Mid-Term Report. MLSDA@AUS-AI 2013: 3 - [c77]Eibe Frank, Bernhard Pfahringer:
Propositionalisation of Multi-instance Data Using Random Forests. Australasian Conference on Artificial Intelligence 2013: 362-373 - [c76]Dino Ienco, Albert Bifet, Indre Zliobaite, Bernhard Pfahringer:
Clustering Based Active Learning for Evolving Data Streams. Discovery Science 2013: 79-93 - [c75]Luís Torgo, Rita P. Ribeiro, Bernhard Pfahringer, Paula Branco:
SMOTE for Regression. EPIA 2013: 378-389 - [c74]Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite:
CD-MOA: Change Detection Framework for Massive Online Analysis. IDA 2013: 92-103 - [c73]Samuel Sarjant, Bernhard Pfahringer, Kurt Driessens, Tony Smith:
A Direct Policy-Search Algorithm for Relational Reinforcement Learning. ILP 2013: 76-92 - [c72]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Towards a Framework for Designing Full Model Selection and Optimization Systems. MCS 2013: 259-270 - [c71]Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes:
Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. ECML/PKDD (1) 2013: 465-479 - [c70]Madeleine Seeland, Stefan Kramer, Bernhard Pfahringer:
Model selection based product kernel learning for regression on graphs. SAC 2013: 136-143 - [c69]Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes:
Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806 - 2012
- [j14]Joaquin Vanschoren, Hendrik Blockeel, Bernhard Pfahringer, Geoffrey Holmes:
Experiment databases - A new way to share, organize and learn from experiments. Mach. Learn. 87(2): 127-158 (2012) - [j13]Jesse Read, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Scalable and efficient multi-label classification for evolving data streams. Mach. Learn. 88(1-2): 243-272 (2012) - [j12]Albert Bifet, Eibe Frank, Geoff Holmes, Bernhard Pfahringer:
Ensembles of Restricted Hoeffding Trees. ACM Trans. Intell. Syst. Technol. 3(2): 30:1-30:20 (2012) - [c68]Quan Sun, Bernhard Pfahringer:
Bagging Ensemble Selection for Regression. Australasian Conference on Artificial Intelligence 2012: 695-706 - [c67]Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read:
Stream Data Mining Using the MOA Framework. DASFAA (2) 2012: 309-313 - [c66]Quan Sun, Bernhard Pfahringer, Michael Mayo:
Full model selection in the space of data mining operators. GECCO (Companion) 2012: 1503-1504 - [c65]Jesse Read, Albert Bifet, Bernhard Pfahringer, Geoff Holmes:
Batch-Incremental versus Instance-Incremental Learning in Dynamic and Evolving Data. IDA 2012: 313-323 - [c64]Madeleine Seeland, Fabian Buchwald, Stefan Kramer, Bernhard Pfahringer:
Maximum Common Subgraph based locally weighted regression. SAC 2012: 165-172 - [c63]Jörg Wicker, Bernhard Pfahringer, Stefan Kramer:
Multi-label classification using boolean matrix decomposition. SAC 2012: 179-186 - [i1]Eibe Frank, Mark A. Hall, Bernhard Pfahringer:
Locally Weighted Naive Bayes. CoRR abs/1212.2487 (2012) - 2011
- [j11]Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank:
Classifier chains for multi-label classification. Mach. Learn. 85(3): 333-359 (2011) - [c62]Bernhard Pfahringer:
Semi-random Model Tree Ensembles: An Effective and Scalable Regression Method. Australasian Conference on Artificial Intelligence 2011: 231-240 - [c61]