Social and Behavioral Sciences Commons^™

Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang

Faculty of Engineering and Information Sciences - Papers: Part A

Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …