A simple idea on applying large regression coefficient to improve the genetic algorithm-PLS for variable selection in multivariate calibration

被引：44

作者：

Yun, Yong-Huan ^{[1
]}

Cao, Dong-Sheng ^{[2
]}

Tan, Min-Li ^{[1
]}

Yan, Jun ^{[1
]}

Ren, Da-Bing ^{[1
]}

Xu, Qing-Song ^{[3
]}

Yu, Ling ^{[4
]}

Liang, Yi-Zeng ^{[1
]}

机构：

[1] Cent South Univ, Coll Chem & Chem Engn, Changsha 410083, Hunan, Peoples R China

[2] Cent South Univ, Coll Pharmaceut Sci, Changsha 410083, Hunan, Peoples R China

[3] Cent South Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China

[4] Shanghai Tobacco Grp Co Ltd, Shanghai 200082, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2014年 / 130卷

关键词：

Genetic algorithm; Variable selection; Partial least squares; Regression coefficient; Multivariate calibration; LEAST-SQUARES REGRESSION; NEAR-INFRARED SPECTRA; WAVELENGTH SELECTION; SUBSET-SELECTION; RANDOM FROG; GA-PLS; ELIMINATION; PREDICTION;

D O I：

10.1016/j.chemolab.2013.09.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Genetic algorithm-based couple with partial least squares (PLS) has been successfully applied for variable selection in multivariate calibration. On the basis of the fact that a large PLS regression coefficient indicates an important variable, a new and simple idea that the structure of a proportion of chromosomes in the initial population is determined by the large regression coefficient is presented in this study. The regression coefficient is obtained by establishing the PLS modeling on the autoscaled data. With this improved approach, the modified GA-PLS method not only makes the optimization better toward the optimal solution, but also obeys the rule of the GAs. The results obtained through investigating one simulated dataset and two near infrared dataset show that the modified method has made much improvement on variable selection compared to the original GA-PLS. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：76 / 83

页数：8

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