Improved SMOTEBagging and Its Application in Imbalanced Data Classification

摘要：

Many real world data mining applications involve imbalanced data sets, When all kinds of data are unevenly distributed and the particular evens of interest may be very few when compared to the other class. Data sets that contain rare evens usually produces biased classifiers that have a higher predictive accuracy over the majority class, but poorer predictive accuracy over the minority class of interest. This paper presents a novel ensemble algorithm with improved SMOTE, and combines selective ensemble with Bagging, which balances the class distribution with improved SMOTEBagging algorithm. Experiments on four UCI data sets and protein-protein interaction experiments mentioned above prove the performance of the method.

关键词： component:SMOTE Bagging SVM Imbalanced Datasets

作者: Zhang Yongqing Zhu Min Zhang Danling Mi Gang Ma Daichuan

作者单位: School of Computer Science Sichuan University Chengdu, Sichuan Province, China School of Life Science, Sichuan University Chengdu, Sichuan Province, China School of Chemistry Scie

会议类型: 国际会议

会议名称: The 13th IEEE Joint International Computer Science and Information Technology Conference(2011年第13届IEEE联合国际计算机科学与信息技术会议 JICSIT 2011)

会议地点: 重庆

会议语种:英文

页码: 1027-1031

在线出版日期: 2011-08-20（万方平台首次上网日期，不代表论文的发表时间）