Novel method for microarray data dimension reduction

被引：0

作者：

Wang, Gang ^{[1
,2
,3
]}

Zhang, Yu-Xuan ^{[4
]}

Li, Ying ^{[1
,2
]}

Chen, Hui-Ling ^{[5
]}

Hu, Wei-Tong ^{[6
]}

Qin, Lei ^{[1
,2
]}

机构：

[1] College of Computer Science and Technology, Jilin University, Changchun,130012, China

[2] Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun,130012, China

[3] College of GeoExploration Science and Technology, Jilin University, Changchun,130026, China

[4] College of Communication Engineering, Jilin University, Changchun,130012, China

[5] College of Physics and Electronic Information, Wenzhou University, Wenzhou,325035, China

[6] Basic Course Department, Air Force Aviation University, Changchun,130022, China

来源：

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2014年 / 44卷 / 05期

关键词：

Gene expression - Diseases - Particle swarm optimization (PSO);

D O I：

10.7964/jdxbgxb201405033

中图分类号：

学科分类号：

摘要：

A two stage parallel gene selection method (TPM) for obtaining the optimal feature subset is proposed. A fuzzy multi-swarm particle optimization (FMP) is also proposed to extend the searching spaces, to overcome the problem of traditional algorithm to be locked to local optimum. The performance of the TMP is evaluated on five microarray datasets (leukemia dataset, colon dataset, breast cancer dataset, lung carcinoma dataset and brain cancer dataset). The comparison results show that the proposed method not only gets better quality of feature subset and higher classification accuracy, but also generates smaller feature subsets. The results of this study could provide a new idea to the field of gene expression.

引用

页码：1429 / 1434

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