Prediction-weighted partial least-squares regression method (PWPLS)

被引：1
|
作者：
Tominaga, Y
Fujiwara, I
机构：
[1] Department of Chemistry I, Discovery Research Laboratories I, Dainippon Pharmaceutical Co., Ltd., Enoki 33-94
来源：
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 1997年 / 38卷 / 02期
关键词：
partial least squares; weighted variables;
D O I：
10.1016/S0169-7439(97)00043-9
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Prediction-weighted partial least-squares (PWPLS) is a progressive approach of partial least-squares (PLS) regression. PWPLS is a simple, efficient, and evolutionary algorithm to select appropriate predictor variables, and weight each selected predictor variable for improving predictability when there is only one dependent variable. We applied PWPLS to two QSAR data sets, and compared the predictability results of PWPLS with that of PLS. (C) 1997 Elsevier Science B.V.
引用
收藏
页码：139 / 144
页数：6
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