Discriminative feature selection with directional outliers correcting for data classification

Lixin Yuan, Guoqiang Yang, Qian Xu, Tong Lu

Abstract: Highlights•Focusing on the directional outliers (DOs).•A Feature Selection via Directional Outliers Correcting (FSDOC) method for supervised feature selection is proposed.•An optimization algorithm captures directional outliers; two correcting algorithms capture redundant features.•Theorems for rationality and justification of algorithms.•The FSDOC method outperforms eight comparative methods on fifteen datasets.