A multi-objective genetic algorithm for text feature selection using the relative discriminative criterion
作者：
Highlights：
• A multi-objective feature selection method called MORDC is proposed for text classification tasks.
• The relevancy of features is computed by using the RDC metric, which is specifically designed for text categorization tasks.
• The relevancy of features is computed by using the text specific RDC metric
• MORDC selects maximum relevant and minimum redundant features.
• MORDC does not employ any learning model to evaluate the effectiveness of selected features.
• The results show that MORDC outperforms the other methods.
摘要
•A multi-objective feature selection method called MORDC is proposed for text classification tasks.•The relevancy of features is computed by using the RDC metric, which is specifically designed for text categorization tasks.•The relevancy of features is computed by using the text specific RDC metric•MORDC selects maximum relevant and minimum redundant features.•MORDC does not employ any learning model to evaluate the effectiveness of selected features.•The results show that MORDC outperforms the other methods.
论文关键词：Text classification,Feature selection,Multi-objective optimization,Relative Discriminative Criterion,Relevancy,Redundancy
论文评审过程：Received 24 January 2019, Revised 11 January 2020, Accepted 3 February 2020, Available online 12 February 2020, Version of Record 13 March 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113276
