Minority identification for imbalanced dataset
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.
classification imbalanced learning feature subsets minority identification
Tong Liu Yongquan Liang Weijian Ni
Department of information Engineering, Shandong University of Science and Technology, Qingdao 266590
国际会议
The 31st Chinese Control Conference(第三十一届中国控制会议)
合肥
英文
3897-3902
2012-07-01(万方平台首次上网日期,不代表论文的发表时间)