Fast hybrid dimensionality reduction method for classification based on feature selection and grouped feature extraction

作者：

Highlights：

• A fast hybrid dimensionality reduction method for classification is proposed.

• Multi-strategy based feature selection is used to filter out irrelevant features.

• Grouped feature extraction is used to remove redundancy among features.

• The proposed method shows excellent efficiency and competitive classification performance.

摘要

•A fast hybrid dimensionality reduction method for classification is proposed.•Multi-strategy based feature selection is used to filter out irrelevant features.•Grouped feature extraction is used to remove redundancy among features.•The proposed method shows excellent efficiency and competitive classification performance.

论文关键词：Dimensionality Reduction,Intrinsic Dimensionality,Feature Selection,Feature Cluster,PCA

论文评审过程：Received 24 June 2019, Revised 2 February 2020, Accepted 3 February 2020, Available online 4 February 2020, Version of Record 15 February 2020.

论文官网地址：https://doi.org/10.1016/j.eswa.2020.113277