| Boosting support vector machines for imbalanced data sets NJ Benjamin X. Wang Knowl. Inf. Syst. 1 (25), 1-20, 2010 | 343* | 2010 |
| Boosting Support Vector Machines for Imbalanced Data Sets NJ Benjamin X. Wang ISMIS, 38-47, 2008 | 343* | 2008 |
| Imbalanced data set learning with synthetic samples BX Wang, N Japkowicz Proc. IRIS machine learning workshop 19, 435, 2004 | 140 | 2004 |
| Improving the interpretability of deep neural networks with knowledge distillation X Liu, X Wang, S Matwin 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 905-912, 2018 | 136 | 2018 |
| Boosting support vector machines for imbalanced data sets BX Wang, N Japkowicz International Symposium on Methodologies for Intelligent Systems, 38-47, 2008 | 78 | 2008 |
| Interpretable deep convolutional neural networks via meta-learning X Liu, X Wang, S Matwin 2018 International Joint Conference on Neural Networks (IJCNN), 1-9, 2018 | 59 | 2018 |
| Vessel route anomaly detection with Hadoop MapReduce SM Xiaoguang Wang, Xuan Liu, Bo Liu, Erico N. de Souza BigData Conference, 25-30, 2014 | 32* | 2014 |
| Cost-Sensitive Boosting Algorithms for Imbalanced Multi-instance Datasets XL Xiaoguang Wang, Stan Matwin, Nathalie Japkowicz Canadian Conference on AI, 174-186, 2013 | 32* | 2013 |
| Meta-MapReduce for scalable data mining SMNJ Xuan Liu, Xiaoguang Wang Journal of Big Data 2 (14), 2015 | 31 | 2015 |
| Resampling and Cost-Sensitive Methods for Imbalanced Multi-instance Learning SM Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz ICDM, 808-816, 2013 | 24* | 2013 |
| A distributed instance-weighted SVM algorithm on large-scale imbalanced datasets SM Xiaoguang Wang, Xuan Liu BigData Conference, 45-51, 2014 | 22* | 2014 |
| Applying instance-weighted support vector machines to class imbalanced datasets NJ Xiaoguang Wang, Xuan Liu, Stan Matwin Xiaoguang Wang, Xuan Liu, Stan Matwin, Nathalie Japkowicz, 112-118, 2014 | 18* | 2014 |
| Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection BN Xiaoguang Wang, Hang Shao, Nathalie Japkowicz, Stan Matwin, Xuan Liu ... ICMLA, 78-82, 2012 | 17* | 2012 |
| Meta-learning for large scale machine learning with MapReduce NJ Xuan Liu, Xiaoguang Wang, Stan Matwin BigData Conference, 105-110, 2013 | 12* | 2013 |
| Automatic Target Recognition using multiple-aspect sonar images BN Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz, Stan Matwin IEEE Congress on Evolutionary Computation, 2330-2337, 2014 | 11* | 2014 |
| Automated approach to classification of mine-like objects using multiple-aspect sonar images X Wang, X Liu, N Japkowicz, S Matwin Journal of Artificial Intelligence and Soft Computing Research 4 (2), 133-148, 2014 | 10 | 2014 |
| Ensemble of Multiple Kernel SVM Classifiers SM Xiaoguang Wang, Xuan Liu, Nathalie Japkowicz Canadian Conference on AI, 239-250, 2014 | 9* | 2014 |
| An Ensemble Method Based on AdaBoost and Meta-Learning SM Xuan Liu, Xiaoguang Wang, Nathalie Japkowicz Canadian Conference on AI, 278-285, 2013 | 9* | 2013 |
| Partition-wise Recurrent Neural Networks for Point-based AIS Trajectory Classification. X Jiang, EN de Souza, X Liu, BH Soleimani, X Wang, DL Silver, S Matwin ESANN, 2017 | 7 | 2017 |
| A multi-view two-level classification method for generalized multi-instance problems HG Xiaoguang Wang, Xuan Liu, Stan Matwin, Nathalie Japkowicz BigData Conference, 104-111, 2014 | 6* | 2014 |
