Article: Variable importance index based on the partial least squares and boxplot cutoff threshold for variable selection Journal: International Journal of Data Analysis Techniques and Strategies (IJDATS) 2017 Vol.9 No.1 pp.34 - 45 Abstract: The variable importance in projection or VIP index obtained by the partial least squares regression (PLS-R) has become a crucial measurement of each predictor to relieve a problem of measuring multiple variables per sample. It has been applied to classification task although it is designed for regression. The new variable importance index combining concept of PLS-R and boxplot cutoff threshold, VIIC-BCT, was here particularly presented for classification of high dimensional data. The proposed VIIC-BCT was compared to the traditional VIP index (VIP-1) and the modified VIP index with boxplot cutoff threshold (VIP-BCT) thru simulation. The four parameters, percentage of the number of relevant variables (Prel), magnitude of mean difference of relevant variables between two classes (Mdif), degree of correlation between relevant variables (Σ) and the sample size (n), were specified to generate the specific 108 situations. The result indicated the VIIC-BCT shows the best performance in the particularly complicated circumstance. Inderscience Publishers - linking academia, business and industry through research

Title: Variable importance index based on the partial least squares and boxplot cutoff threshold for variable selection

Authors: Noppamas Akarachantachote; Seree Chadcham; Kidakan Saithanu

Addresses: Division of Mathematics and Statistics, Huachiew Chalermprakiet University, Samutprakarn, Thailand ' College of Research Methodology and Cognitive Science, Burapha University, Chonburi, Thailand ' Department of Mathematics, Burapha University, Chonburi, Thailand

Abstract: The variable importance in projection or VIP index obtained by the partial least squares regression (PLS-R) has become a crucial measurement of each predictor to relieve a problem of measuring multiple variables per sample. It has been applied to classification task although it is designed for regression. The new variable importance index combining concept of PLS-R and boxplot cutoff threshold, VIIC-BCT, was here particularly presented for classification of high dimensional data. The proposed VIIC-BCT was compared to the traditional VIP index (VIP-1) and the modified VIP index with boxplot cutoff threshold (VIP-BCT) thru simulation. The four parameters, percentage of the number of relevant variables (Prel), magnitude of mean difference of relevant variables between two classes (Mdif), degree of correlation between relevant variables (Σ) and the sample size (n), were specified to generate the specific 108 situations. The result indicated the VIIC-BCT shows the best performance in the particularly complicated circumstance.

Keywords: variable selection; data classification; partial least squares; PLS regression; PLS-R; variable importance in projection; VIP index; VIP-BCT; VIIC-BCT; boxplot cutoff threshold; multiple variables; high dimensional data.

DOI: 10.1504/IJDATS.2017.083063

International Journal of Data Analysis Techniques and Strategies, 2017 Vol.9 No.1, pp.34 - 45

Received: 21 May 2015
Accepted: 05 Nov 2015
Published online: 20 Mar 2017 *

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Title: Variable importance index based on the partial least squares and boxplot cutoff threshold for variable selection

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