| The WEKA data mining software: an update M Hall, E Frank, G Holmes, B Pfahringer, P Reutemann, IH Witten ACM SIGKDD explorations newsletter 11 (1), 10-18, 2009 | 25779 | 2009 |
| Artificial Intelligence: Second CCF International Conference, ICAI 2019, Xuzhou, China, August 22-23, 2019, Proceedings K Knight, C Zhang, G Holmes, ML Zhang Springer, 2019 | 6922 | 2019 |
| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85 (3), 333-359, 2011 | 2737 | 2011 |
| Moa: Massive online analysis, a framework for stream classification and clustering A Bifet, G Holmes, B Pfahringer, P Kranen, H Kremer, T Jansen, T Seidl Proceedings of the first workshop on applications of pattern analysis, 44-50, 2010 | 2430 | 2010 |
| Benchmarking attribute selection techniques for discrete class data mining MA Hall, G Holmes IEEE Transactions on Knowledge and Data engineering 15 (6), 1437-1447, 2003 | 1696 | 2003 |
| Weka: A machine learning workbench G Holmes, A Donkin, IH Witten Proceedings of ANZIIS'94-Australian New Zealnd Intelligent Information …, 1994 | 1607 | 1994 |
| Data mining in bioinformatics using Weka E Frank, M Hall, L Trigg, G Holmes, IH Witten Bioinformatics 20 (15), 2479-2481, 2004 | 1194 | 2004 |
| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Joint European conference on machine learning and knowledge discovery in …, 2009 | 1153 | 2009 |
| Adaptive random forests for evolving data stream classification HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ... Machine Learning 106 (9), 1469-1495, 2017 | 976 | 2017 |
| Weka-a machine learning workbench for data mining E Frank, M Hall, G Holmes, R Kirkby, B Pfahringer, IH Witten, L Trigg Data mining and knowledge discovery handbook, 1269-1277, 2010 | 885 | 2010 |
| New ensemble methods for evolving data streams A Bifet, G Holmes, B Pfahringer, R Kirkby, R Gavalda Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009 | 872 | 2009 |
| Multinomial naive bayes for text categorization revisited AM Kibriya, E Frank, B Pfahringer, G Holmes Australasian joint conference on artificial intelligence, 488-499, 2004 | 703 | 2004 |
| Multi-label classification using ensembles of pruned sets J Read, B Pfahringer, G Holmes 2008 eighth IEEE international conference on data mining, 995-1000, 2008 | 628 | 2008 |
| Using model trees for classification E Frank, Y Wang, S Inglis, G Holmes, IH Witten Machine learning 32 (1), 63-76, 1998 | 556 | 1998 |
| Leveraging bagging for evolving data streams A Bifet, G Holmes, B Pfahringer Joint European conference on machine learning and knowledge discovery in …, 2010 | 532 | 2010 |
| WEKA---Experiences with a Java Open-Source Project RR Bouckaert, E Frank, MA Hall, G Holmes, B Pfahringer, P Reutemann, ... The Journal of Machine Learning Research 11, 2533-2541, 2010 | 473 | 2010 |
| Active learning with drifting streaming data I Žliobaitė, A Bifet, B Pfahringer, G Holmes IEEE transactions on neural networks and learning systems 25 (1), 27-39, 2013 | 468 | 2013 |
| Machine learning for data streams: with practical examples in MOA A Bifet, R Gavalda, G Holmes, B Pfahringer MIT press, 2023 | 452 | 2023 |
| Generating rule sets from model trees G Holmes, M Hall, E Frank Australasian joint conference on artificial intelligence, 1-12, 1999 | 422 | 1999 |
| Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes Journal of Machine Learning Research 17 (21), 1-5, 2016 | 360 | 2016 |
