| Isolation forest FT Liu, KM Ting, ZH Zhou 2008 eighth ieee international conference on data mining, 413-422, 2008 | 9642 | 2008 |
| Isolation-based anomaly detection FT Liu, KM Ting, ZH Zhou ACM Transactions on Knowledge Discovery from Data (TKDD) 6 (1), 1-39, 2012 | 2691 | 2012 |
| Issues in stacked generalization KM Ting, IH Witten Journal of artificial intelligence research 10, 271-289, 1999 | 1049 | 1999 |
| Confusion matrix KM Ting Encyclopedia of machine learning and data mining, 1-1, 2016 | 897 | 2016 |
| An instance-weighting method to induce cost-sensitive trees KM Ting IEEE Transactions on Knowledge and Data Engineering 14 (3), 659-665, 2002 | 670 | 2002 |
| A survey of audio-based music classification and annotation Z Fu, G Lu, KM Ting, D Zhang IEEE transactions on multimedia 13 (2), 303-319, 2010 | 662 | 2010 |
| Fast anomaly detection for streaming data SC Tan, KM Ting, TF Liu IJCAI proceedings-international joint conference on artificial intelligence …, 2011 | 428 | 2011 |
| A comparative study of cost-sensitive boosting algorithms KM Ting Proceedings of the seventeenth international conference on machine learning …, 2000 | 412 | 2000 |
| Precision and recall KM Ting Encyclopedia of machine learning, 781-781, 2011 | 371 | 2011 |
| Stacking bagged and dagged models KM Ting, IH Witten | 368 | 1997 |
| Stacked Generalization: when does it work? KM Ting, IH Witten Department of Computer Science, University of Waik, 1997 | 284 | 1997 |
| z-SVM: An SVM for improved classification of imbalanced data T Imam, KM Ting, J Kamruzzaman Australasian joint conference on artificial intelligence, 264-273, 2006 | 218 | 2006 |
| Isolation‐based anomaly detection using nearest‐neighbor ensembles TR Bandaragoda, KM Ting, D Albrecht, FT Liu, Y Zhu, JR Wells Computational Intelligence 34 (4), 968-998, 2018 | 214 | 2018 |
| On detecting clustered anomalies using sciforest FT Liu, KM Ting, ZH Zhou Joint European conference on machine learning and knowledge discovery in …, 2010 | 204 | 2010 |
| Density-ratio based clustering for discovering clusters with varying densities Y Zhu, KM Ting, MJ Carman Pattern Recognition 60, 983-997, 2016 | 158 | 2016 |
| Classification under streaming emerging new classes: A solution using completely-random trees X Mu, KM Ting, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 29 (8), 1605-1618, 2017 | 153 | 2017 |
| Inducing cost-sensitive trees via instance weighting KM Ting European symposium on principles of data mining and knowledge discovery, 139-147, 1998 | 145 | 1998 |
| On the application of ROC analysis to predict classification performance under varying class distributions GI Webb, KM Ting Machine learning 58 (1), 25-32, 2005 | 136 | 2005 |
| Multi-label learning with emerging new labels Y Zhu, KM Ting, ZH Zhou IEEE Transactions on Knowledge and Data Engineering 30 (10), 1901-1914, 2018 | 126 | 2018 |
| Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification GI Webb, JR Boughton, F Zheng, KM Ting, H Salem Machine learning 86 (2), 233-272, 2012 | 126 | 2012 |
Zhi-Hua ZhouNanjing UniversityVerified email at nju.edu.cn
