A Kernel Partial least square based feature selection method

作者：

Highlights：

• The paper proposes a Kernel Partial Least Square (KPLS) based Feature Selection Method aiming for easy computation and improving classification accuracy for high dimensional data.

• The proposed method makes use of KPLS regression coefficients to identify an optimal set of features, thus avoiding non-linear optimization.

• Experiments were carried out on seven real life datasets with four different classifiers: SVM, LDA, Random Forest and Naïve Bayes.

• Experimental results highlight the advantage of the proposed method over several competing feature selection techniques.

摘要

•The paper proposes a Kernel Partial Least Square (KPLS) based Feature Selection Method aiming for easy computation and improving classification accuracy for high dimensional data.•The proposed method makes use of KPLS regression coefficients to identify an optimal set of features, thus avoiding non-linear optimization.•Experiments were carried out on seven real life datasets with four different classifiers: SVM, LDA, Random Forest and Naïve Bayes.•Experimental results highlight the advantage of the proposed method over several competing feature selection techniques.

论文关键词：Feature selection,Kernel partial least square,Regression coefficients,Relevance,Classification

论文评审过程：Received 20 April 2017, Revised 23 April 2018, Accepted 13 May 2018, Available online 18 May 2018, Version of Record 28 May 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.012