Feature selection using a combination of genetic algorithm and selection frequency curve analysis

被引：5

作者：

Yang, Qianxu ^{[1
,2
]}

Wang, Meng ^{[1
]}

Xiao, Hongbin ^{[2
]}

Yang, Lei ^{[3
]}

Zhu, Baokun ^{[1
]}

Zhang, Tiandong ^{[1
]}

Zeng, Xiaoying ^{[1
]}

机构：

[1] China Tobacco Yunnan Ind Co Ltd, R&D Ctr, Kunming 650231, Peoples R China

[2] Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China

[3] Kunming Univ Sci & Technol, Fac Environm Sci & Engn, Kunming 650500, Peoples R China

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2015年 / 148卷

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

Genetic algorithm; Background correction; Selected frequency curve; Feature selection; MULTIVARIATE CALIBRATION; VARIABLE SELECTION; HERBAL MEDICINE; PLS; PREDICTION; COMPONENTS; REGRESSION; DISCOVERY;

D O I：

10.1016/j.chemolab.2015.09.007

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 (GA) is a search heuristic that is commonly used for feature selection. The main drawback of GA lies in its unstable results for a random initialization population and background correlation; robust results can only be obtained through a series of runs. This paper proposes the use of selected frequency curve (SFC) analysis to evaluate variable importance based on the results of a classical GA. Three statistical parameters are proposed for the quantitative definition of variable importance based on the SFC. The proposed method was applied to three benchmarking datasets obtained from previous works. This was done in conjunction with the use of different regression and classification methods, and the results were compared with those of a classical GA. The results revealed the robustness and superiority of the combination of GA and SFC analyses (GA-SFC) compared with the use of classical GA. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：106 / 114

页数：9

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