Minority identification for imbalanced dataset

摘要：

Minority identification is an important issue in network security and financial applications. This paper considers the direct maximum reachability distance of an object and the indirect minimum reachability distance of an object for measuring the degree of an object being minority. The data classification is performed by an optimized combination model. We empirically evaluate the proposed approach using a number of UCI data sets, and experiment results demonstrate that our method outperforms the existing methods in terms of the comparisons of ROC curves.

关键词： classification imbalanced learning feature subsets minority identification

作者: Tong Liu Yongquan Liang Weijian Ni

作者单位: Department of information Engineering, Shandong University of Science and Technology, Qingdao 266590

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 3897-3902

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）