Improved multiclass feature selection via list combination
作者：
Highlights：
• We introduce new SVM-RFE feature selection methods for multiclass problems.
• We use binary decomposition followed by strategies to combine lists of features.
• We discuss statistical approaches and voting theory methods.
• One-vs-One methods give better results than One-vs-All methods.
• The new K-First method is the more effective in selecting relevant features.
摘要
•We introduce new SVM-RFE feature selection methods for multiclass problems.•We use binary decomposition followed by strategies to combine lists of features.•We discuss statistical approaches and voting theory methods.•One-vs-One methods give better results than One-vs-All methods.•The new K-First method is the more effective in selecting relevant features.
论文关键词：Feature selection,Multiclass problems,Support vector machine
论文评审过程：Received 8 March 2017, Revised 30 June 2017, Accepted 30 June 2017, Available online 6 July 2017, Version of Record 10 July 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.06.043
