A label-specific multi-label feature selection algorithm based on the Pareto dominance concept
作者：
Highlights：
• Unlike common multi-label feature selection, the proposed method derives from different cognitive standpoint.
• Feature selection process is directly done on multi-label data, and there is no need to data transformation.
• The proposed method tries to find label-specific features which are the most discriminative features for each label.
• Also, an extension of our method is presented which selects a pre-defined number of features.
• The proposed method is appropriate to both numerical and nominal features.
• The proposed method is effective and fast.
摘要
•Unlike common multi-label feature selection, the proposed method derives from different cognitive standpoint.•Feature selection process is directly done on multi-label data, and there is no need to data transformation.•The proposed method tries to find label-specific features which are the most discriminative features for each label.•Also, an extension of our method is presented which selects a pre-defined number of features.•The proposed method is appropriate to both numerical and nominal features.•The proposed method is effective and fast.
论文关键词：Multi-label dataset,Feature selection,Label-specific features,Pareto dominance,Online feature selection
论文评审过程：Received 21 March 2018, Revised 28 October 2018, Accepted 16 December 2018, Available online 17 December 2018, Version of Record 21 December 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.12.020
