Rough set model based feature selection for mixed-type data with feature space decomposition

作者：

Highlights：

• Interpretability of the feature selection for mixed-type data is increased.

• Any transforming procedure is not needed on categorical and numerical features.

• FSMSD selects features that are not biased by any data-type.

• FSMSD and benchmark methods are compared with 15 mixed-type data.

摘要

•Interpretability of the feature selection for mixed-type data is increased.•Any transforming procedure is not needed on categorical and numerical features.•FSMSD selects features that are not biased by any data-type.•FSMSD and benchmark methods are compared with 15 mixed-type data.

论文关键词：Feature selection,Mixed-type data,Classification,Rough set model,Feature space decomposition

论文评审过程：Received 13 November 2017, Revised 25 February 2018, Accepted 7 March 2018, Available online 8 March 2018, Version of Record 20 March 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.03.010