| Ensemble learning for data stream analysis: A survey B Krawczyk, LL Minku, J Gama, J Stefanowski, M Woźniak Information Fusion 37, 132-156, 2017 | 835 | 2017 |
| The impact of diversity on online ensemble learning in the presence of concept drift LL Minku, AP White, X Yao IEEE Transactions on Knowledge and Data Engineering 22 (5), 730-742, 2010 | 514 | 2010 |
| DDD: A New Ensemble Approach For Dealing With Concept Drift L Minku, X Yao Knowledge and Data Engineering, IEEE Transactions on 24 (4), 619-633, 2012 | 481 | 2012 |
| Resampling-based ensemble methods for online class imbalance learning S Wang, LL Minku, X Yao IEEE Transactions on Knowledge and Data Engineering 27 (5), 1356-1368, 2015 | 342 | 2015 |
| A systematic study of online class imbalance learning with concept drift S Wang, LL Minku, X Yao IEEE transactions on neural networks and learning systems 29 (10), 4802-4821, 2018 | 230 | 2018 |
| Ensembles and locality: Insight on improving software effort estimation LL Minku, X Yao Information and Software Technology 55 (8), 1512-1528, 2013 | 159 | 2013 |
| Online Ensemble Learning of Data Streams with Gradually Evolved Classes Y Sun, K Tang, LL Minku, S Wang, X Yao IEEE Transactions on Knowledge and Data Engineering 28 (6), 1532-1545, 2016 | 153 | 2016 |
| A learning framework for online class imbalance learning S Wang, LL Minku, X Yao 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning …, 2013 | 124 | 2013 |
| Software effort estimation as a multiobjective learning problem LL Minku, X Yao ACM Transactions on Software Engineering and Methodology (TOSEM) 22 (4), 1-32, 2013 | 109 | 2013 |
| Next challenges for adaptive learning systems I Zliobaite, A Bifet, M Gaber, B Gabrys, J Gama, L Minku, K Musial ACM SIGKDD Explorations Newsletter 14 (1), 48-55, 2012 | 98 | 2012 |
| Concept drift detection for online class imbalance learning S Wang, LL Minku, D Ghezzi, D Caltabiano, P Tino, X Yao The 2013 International Joint Conference on Neural Networks (IJCNN), 1-10, 2013 | 90 | 2013 |
| An empirical evaluation of ensemble adjustment methods for analogy-based effort estimation M Azzeh, AB Nassif, LL Minku Journal of Systems and Software 103, 36-52, 2015 | 86 | 2015 |
| The impact of parameter tuning on software effort estimation using learning machines L Song, LL Minku, X Yao Proceedings of the 9th international conference on predictive models in …, 2013 | 86 | 2013 |
| A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling XN Shen, LL Minku, N Marturi, YN Guo, Y Han Information Sciences 428, 1-29, 2018 | 75 | 2018 |
| Sharing data and models in software engineering T Menzies, E Kocaguneli, B Turhan, L Minku, F Peters Morgan Kaufmann, 2014 | 73 | 2014 |
| How to make best use of cross-company data in software effort estimation? LL Minku, X Yao Proceedings of the 36th International Conference on Software Engineering …, 2014 | 67 | 2014 |
| Dealing with Multiple Classes in Online Class Imbalance Learning S Wang, LL Minku, X Yao Proc. 25th Int. Joint Conf. Artificial Intelligence, IJCAI/AAAI Press, 2118-2124, 2016 | 65 | 2016 |
| Online class imbalance learning and its applications in fault detection S Wang, LL Minku, X Yao International Journal of Computational Intelligence and Applications 12 (04 …, 2013 | 65 | 2013 |
| The Handbook of Engineering Self-Aware and Self-Expressive Systems T Chen, F Faniyi, R Bahsoon, PR Lewis, X Yao, LL Minku, L Esterle arXiv preprint arXiv:1409.1793, 2014 | 64 | 2014 |
| Improved Evolutionary Algorithm Design for the Project Scheduling Problem Based on Runtime Analysis L Minku, D Sudholt, X Yao IEEE Transactions on Software Engineering 40 (1), 83-102, 2014 | 54 | 2014 |
