| Detecting concept change in dynamic data streams: A sequential approach based on reservoir sampling R Pears, S Sakthithasan, YS Koh Machine learning 97, 259-293, 2014 | 133 | 2014 |
| Detecting volatility shift in data streams DTJ Huang, YS Koh, G Dobbie, R Pears 2014 IEEE International Conference on Data Mining, 863-868, 2014 | 100 | 2014 |
| Research Methods-2 GA Raj, R Pears, P Carter, N Kasabov Auckland University of Technology, 2001 | 68 | 2001 |
| Synthetic Minority over-sampling technique (SMOTE) for predicting software build outcomes R Pears, J Finlay, AM Connor arXiv preprint arXiv:1407.2330, 2014 | 59 | 2014 |
| Data stream mining for predicting software build outcomes using source code metrics J Finlay, R Pears, AM Connor Information and Software Technology 56 (2), 183-198, 2014 | 58 | 2014 |
| Weighted association rule mining via a graph based connectivity model R Pears, YS Koh, G Dobbie, W Yeap Information Sciences 218, 61-84, 2013 | 57 | 2013 |
| A smart system for short-term price prediction using time series models HV Nguyen, MA Naeem, N Wichitaksorn, R Pears Computers & Electrical Engineering 76, 339-352, 2019 | 55 | 2019 |
| Use of hoeffding trees in concept based data stream mining S Hoeglinger, R Pears 2007 third international conference on information and automation for …, 2007 | 51 | 2007 |
| One pass concept change detection for data streams S Sakthithasan, R Pears, YS Koh Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia …, 2013 | 50 | 2013 |
| Contextual relevance feedback in web information retrieval DK Limbu, A Connor, R Pears, S MacDonell Proceedings of the 1st International Conference on Information interaction …, 2006 | 42 | 2006 |
| Multiple time-series prediction through multiple time-series relationships profiling and clustered recurring trends H Widiputra, R Pears, N Kasabov Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia …, 2011 | 37 | 2011 |
| Enhancing random projection with independent and cumulative additive noise for privacy-preserving data stream mining B Denham, R Pears, MA Naeem Expert Systems with Applications 152, 113380, 2020 | 35 | 2020 |
| Weighted association rule mining using particle swarm optimization R Pears, YS Koh New Frontiers in Applied Data Mining: PAKDD 2011 International Workshops …, 2012 | 35 | 2012 |
| SENTIPEDE: A Smart System for Sentiment-based Personality Detection from Short Texts. A Darliansyah, MA Naeem, F Mirza, R Pears J. Univers. Comput. Sci. 25 (10), 1323-1352, 2019 | 29 | 2019 |
| Analyzing and repairing concept drift adaptation in data stream classification B Halstead, YS Koh, P Riddle, R Pears, M Pechenizkiy, A Bifet, G Olivares, ... Machine Learning 111 (10), 3489-3523, 2022 | 28 | 2022 |
| Valency based weighted association rule mining YS Koh, R Pears, W Yeap Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia …, 2010 | 28 | 2010 |
| Rare association rule mining via transaction clustering YS Koh, R Pears Proceedings of the 7th Australasian Data Mining Conference-Volume 87, 87-94, 2008 | 28 | 2008 |
| Discovering diverse association rules from multidimensional schema M Usman, R Pears, ACM Fong Expert systems with applications 40 (15), 5975-5996, 2013 | 25 | 2013 |
| CBDT: a concept based approach to data stream mining S Hoeglinger, R Pears, YS Koh Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia …, 2009 | 23 | 2009 |
| A composite spatio-temporal modeling approach for age invariant face recognition FB Alvi, R Pears Expert Systems with Applications 72, 383-394, 2017 | 21 | 2017 |
Yun Sing KohThe University of AucklandПідтверджена електронна адреса в auckland.ac.nz
