Fully component selection: An efficient combination of feature selection and principal component analysis to increase model performance

作者：

Highlights：

• FCS is proposed as a complementary step for principal component analysis.

• PCA-FCS identifies the most relevant components based on their level of contribution.

• PCA-FCS simultaneously performs dimension reduction and component selection.

• PCA-FCS has substantially improved the accuracy of the random forest algorithm.

摘要

•FCS is proposed as a complementary step for principal component analysis.•PCA-FCS identifies the most relevant components based on their level of contribution.•PCA-FCS simultaneously performs dimension reduction and component selection.•PCA-FCS has substantially improved the accuracy of the random forest algorithm.

论文关键词：High dimensional data,Dimension reduction,Random forest,Spectroscopic data,Principal component analysis

论文评审过程：Received 26 October 2020, Revised 16 July 2021, Accepted 25 July 2021, Available online 29 July 2021, Version of Record 13 August 2021.

论文官网地址：https://doi.org/10.1016/j.eswa.2021.115678