Improved leaps and bounds variable selection algorithm based on principal component analysis

被引：1

作者：

Zhang, Wenjun ^{[1
]}

Wang, Xin ^{[1
]}

Chen, Lin ^{[1
]}

机构：

[1] Hebei Univ Technol, Sch Chem Engn, Dept Appl Chem, Tianjin 300130, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2014年 / 139卷

关键词：

Feature selection; Variable selection; Multiple linear regression; Leaps and bounds; FEATURE SUBSET-SELECTION; GENETIC ALGORITHMS; EVOLUTIONARY; QSAR;

D O I：

10.1016/j.chemolab.2014.09.017

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a new variable selection algorithm is described, based on leaps and bounds regression. The algorithm removes the limit of the traditional algorithm that the descriptors must be less than the samples, by replacing the original variables in a subset evaluation with a small number of principal components. Two different sizes of variables data sets were employed to investigate the performance of the new algorithm. The result shows that the improved algorithm can obtain optimal or good sub-optimal subsets when a different number of principal components are used. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：76 / 83

页数：8

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