| Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85 (3), 333, 2011 | 1432 | 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 | 712 | 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 | 402 | 2008 |
| A pruned problem transformation method for multi-label classification J Read Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS …, 2008 | 233 | 2008 |
| Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes The Journal of Machine Learning Research 17 (1), 667-671, 2016 | 217 | 2016 |
| Adaptive random forests for evolving data stream classification HM Gomes, A Bifet, J Read, JP Barddal, F Enembreck, B Pfharinger, ... Machine Learning 106 (9-10), 1469-1495, 2017 | 209 | 2017 |
| 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 | 132 | 2015 |
| Scalable and efficient multi-label classification for evolving data streams J Read, A Bifet, G Holmes, B Pfahringer Machine Learning 88 (1-2), 243-272, 2012 | 125 | 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 | 108 | 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 | 101 | 2017 |
| Scalable multi-label classification J Read University of Waikato, 2010 | 101 | 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 | 97 | 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 | 97 | 2014 |
| 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 | 88 | 2018 |
| 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 | 81 | 2013 |
| 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 | 73 | 2015 |
| 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 | 71 | 2015 |
| 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 | 71 | 2013 |
| A distributed particle filter for nonlinear tracking in wireless sensor networks J Read, K Achutegui, J Míguez Signal Processing 98, 121-134, 2014 | 60 | 2014 |
| On the flexibility of the design of multiple try Metropolis schemes L Martino, J Read Computational Statistics 28 (6), 2797-2823, 2013 | 56 | 2013 |
Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
