| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85 (3), 333-359, 2011 | 1796 | 2011 |
| 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 | 861 | 2009 |
| 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 | 485 | 2008 |
| 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 | 394 | 2017 |
| Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes | 269 | 2016 |
| A pruned problem transformation method for multi-label classification J Read Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS …, 2008 | 268 | 2008 |
| Scikit-multiflow: A multi-output streaming framework J Montiel, J Read, A Bifet, T Abdessalem The Journal of Machine Learning Research 19 (1), 2915-2914, 2018 | 218 | 2018 |
| Efficient online evaluation of big data stream classifiers A Bifet, G de Francisci Morales, J Read, G Holmes, B Pfahringer Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015 | 179 | 2015 |
| Scalable and efficient multi-label classification for evolving data streams J Read, A Bifet, G Holmes, B Pfahringer Machine Learning 88 (1), 243-272, 2012 | 145 | 2012 |
| Batch-incremental versus instance-incremental learning in dynamic and evolving data J Read, A Bifet, B Pfahringer, G Holmes International symposium on intelligent data analysis, 313-323, 2012 | 138 | 2012 |
| Cooperative parallel particle filters for online model selection and applications to urban mobility L Martino, J Read, V Elvira, F Louzada Digital Signal Processing 60, 172-185, 2017 | 126 | 2017 |
| Scalable multi-label classification J Read University of Waikato, 2010 | 125 | 2010 |
| Evaluation methods and decision theory for classification of streaming data with temporal dependence I Žliobaitė, A Bifet, J Read, B Pfahringer, G Holmes Machine Learning 98 (3), 455-482, 2015 | 118 | 2015 |
| Efficient monte carlo methods for multi-dimensional learning with classifier chains J Read, L Martino, D Luengo Pattern Recognition 47 (3), 1535-1546, 2014 | 114 | 2014 |
| Pitfalls in benchmarking data stream classification and how to avoid them A Bifet, J Read, I Žliobaitė, B Pfahringer, G Holmes Joint European conference on machine learning and knowledge discovery in …, 2013 | 99 | 2013 |
| Efficient data stream classification via probabilistic adaptive windows A Bifet, B Pfahringer, J Read, G Holmes Proceedings of the 28th annual ACM symposium on applied computing, 801-806, 2013 | 93 | 2013 |
| Independent doubly adaptive rejection Metropolis sampling within Gibbs sampling L Martino, J Read, D Luengo IEEE Transactions on Signal Processing 63 (12), 3123-3138, 2015 | 90 | 2015 |
| Machine learning for streaming data: state of the art, challenges, and opportunities HM Gomes, J Read, A Bifet, JP Barddal, J Gama ACM SIGKDD Explorations Newsletter 21 (2), 6-22, 2019 | 86 | 2019 |
| Scalable multi-output label prediction: From classifier chains to classifier trellises J Read, L Martino, PM Olmos, D Luengo Pattern Recognition 48 (6), 2096-2109, 2015 | 82 | 2015 |
| A distributed particle filter for nonlinear tracking in wireless sensor networks J Read, K Achutegui, J Miguez Signal Processing 98, 121-134, 2014 | 67 | 2014 |
Albert BifetTélécom ParisTech & University of WaikatoVerified email at telecom-paristech.fr
