Feature selection for regression problems based on the Morisita estimator of intrinsic dimension
作者：
Highlights：
• A new supervised filter for regression problems is proposed.
• The filter uses the newly introduced Morisita estimator of intrinsic dimension.
• The filter distinguishes between relevant, irrelevant and redundant features.
• The filter is comprehensively validated using real and simulated datasets.
• A generic methodology for validating and comparing filters is suggested.
摘要
•A new supervised filter for regression problems is proposed.•The filter uses the newly introduced Morisita estimator of intrinsic dimension.•The filter distinguishes between relevant, irrelevant and redundant features.•The filter is comprehensively validated using real and simulated datasets.•A generic methodology for validating and comparing filters is suggested.
论文关键词：Feature selection,Intrinsic dimension,Morisita index,Measure of relevance,Data mining
论文评审过程：Received 22 March 2016, Revised 8 May 2017, Accepted 9 May 2017, Available online 10 May 2017, Version of Record 18 May 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.05.008
