报告人：邓少军 指导老师：林子雨 时间： 2015 年 8 月 7 日 厦门大学数据库实验室 论文阅读汇报

01 Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights 02 Cost-sensitive decision tree ensembles for effective imbalanced classification Contents 目 录

第 章 1 Learning Imbalanced Multi-class Data with Optimal Dichotomy Weights

1 2 论文 参考文献 2013 IEEE 13th International Conference on Data Mining Xu-Ying Liu, Qian-Qian Li and Zhi-Hua Zhou ， Key Laboratory of Computer Network and Information Integration, MOE, Southeast University, China National Key Laboratory for Novel Software Technology, Nanjing University, China 主要内容 In this paper ， we propose the imECOC method which works on dichotomies to handle both the betweenclass imbalance and within-class imbalance.

第 章 2 Cost-sensitive decision tree ensembles for effective imbalanced classification

1 2 论文 参考文献 Applied Soft Computing 14 （ 2014 ） Bartosz Krawczyk, Michał Wozniak, Gerald Schaefer Department of Systems and Computer Networks, Wroclaw University of Technology, Poland Department of Computer Science, Loughborough University, Loughborough, UK 主要内容 In this paper, we introduce an effective ensemble of costsensitive decision trees for imbalanced classification. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process.

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