| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85, 333-359, 2011 | 2062 | 2011 |
| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009 | 950 | 2009 |
| Adaptive random forests for evolving data stream classification HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ... Machine Learning 106, 1469-1495, 2017 | 536 | 2017 |
| 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 | 530 | 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 | 321 | 2018 |
| Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes | 296 | 2016 |
| A pruned problem transformation method for multi-label classification J Read Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS …, 2008 | 290 | 2008 |
| 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 | 201 | 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 | 164 | 2019 |
| Scalable and efficient multi-label classification for evolving data streams J Read, A Bifet, G Holmes, B Pfahringer Machine Learning 88, 243-272, 2012 | 155 | 2012 |
| Batch-incremental versus instance-incremental learning in dynamic and evolving data J Read, A Bifet, B Pfahringer, G Holmes Advances in Intelligent Data Analysis XI: 11th International Symposium, IDA …, 2012 | 145 | 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 | 143 | 2017 |
| Scalable multi-label classification J Read University of Waikato, 2010 | 135 | 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, 455-482, 2015 | 133 | 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 | 123 | 2014 |
| Pitfalls in benchmarking data stream classification and how to avoid them A Bifet, J Read, I Žliobaitė, B Pfahringer, G Holmes Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 109 | 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 | 106 | 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 | 104 | 2015 |
| 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 | 93 | 2015 |
| River: machine learning for streaming data in python J Montiel, M Halford, SM Mastelini, G Bolmier, R Sourty, R Vaysse, ... The Journal of Machine Learning Research 22 (1), 4945-4952, 2021 | 92 | 2021 |
Albert BifetTélécom ParisTech & University of WaikatoDirección de correo verificada de telecom-paristech.fr
