A Filter Based Feature Selection Algorithm Using Null Space of Covariance Matrix for DNA Microarray Gene Expression Data

被引：20

作者：

Sharma, Alok ^{[1
,2
,3
]}

Imoto, Seiya ^{[1
]}

Miyano, Satoru ^{[1
]}

机构：

[1] Univ Tokyo, Ctr Human Genome, Lab DNA Informat Anal, Tokyo 1138654, Japan

[2] Griffith Univ, Signal Proc Lab, Nathan, Qld 4111, Australia

[3] Univ S Pacific, Sch Engn & Phys, Suva, Fiji

来源：

CURRENT BIOINFORMATICS | 2012年 / 7卷 / 03期

关键词：

Cancer classification; covariance matrix; DNA microarray gene expression data; feature or gene selection; Filter based method; null space; SINGULAR-VALUE DECOMPOSITION; SAMPLE-SIZE PROBLEM; CLASSIFICATION; CANCER; PREDICTION; LDA;

D O I：

10.2174/157489312802460802

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We propose a new filter based feature selection algorithm for classification based on DNA microarray gene expression data. It utilizes null space of covariance matrix for feature selection. The algorithm can perform bulk reduction of features (genes) while maintaining the quality information in the reduced subset of features for discriminative purpose. Thus, it can be used as a pre-processing step for other feature selection algorithms. The algorithm does not assume statistical independency among the features. The algorithm shows promising classification accuracy when compared with other existing techniques on several DNA microarray gene expression datasets.

引用

页码：289 / 294

页数：6

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