Discriminative feature selection with directional outliers correcting for data classification
作者：
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.
摘要
•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.
论文关键词：Feature selection,Directional outlier,Redundant features,Deviation,Supervised method
论文评审过程：Received 13 July 2021, Revised 23 December 2021, Accepted 14 January 2022, Available online 22 January 2022, Version of Record 6 February 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108541
