A differential evolution based feature combination selection algorithm for high-dimensional data

被引：37

作者：

Guan, Boxin ^{[1
,2
]}

Zhao, Yuhai ^{[1
,2
]}

Yin, Ying ^{[1
,2
]}

Li, Yuan ^{[3
]}

机构：

[1] Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Peoples R China

[2] Northeastern Univ, Key Lab Med Image Comp, Minist Educ, Shenyang 110819, Peoples R China

[3] North China Univ Technol, Sch Informat Sci & Technol, Beijing 100144, Peoples R China

来源：

INFORMATION SCIENCES | 2021年 / 547卷

关键词：

Binary space partitioning; Differential evolution; Feature combination; High-dimensional data; DETECTING EPISTATIC INTERACTIONS; GENOME-WIDE ASSOCIATION; OPTIMIZATION ALGORITHM;

D O I：

10.1016/j.ins.2020.08.081

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Feature combination selection is used in object classification to select complementary features that can produce a powerful combination. One active area of selecting feature combinations is genome-wide association studies (GWAS). However, selecting feature combinations from high-dimensional GWAS data faces a serious issue of high computational complexity. In this paper, a fast evolutionary optimization method named search history-guided differential evolution (HGDE) is proposed to deal with the problem. This method applies the search history memorized in a binary space partitioning tree to enhance its power for selecting feature combinations. We perform a comparative study on the proposed HGDE algorithm and other state-of-the-art algorithms using synthetic datasets, and later employ the HGDE algorithm in experiments on a real age-related macular degeneration dataset. The experimental results show that this proposed algorithm has superior performance in the selection of feature combinations. Moreover, the results provide a reference for studying the functional mechanisms of age-related macular degeneration. (C) 2020 Elsevier Inc. All rights reserved.

引用

页码：870 / 886

页数：17

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