Null space based feature selection method for gene expression data

Sharma, Alokanand and Imoto, S. and Miyano, S. and Sharma, V. (2012) Null space based feature selection method for gene expression data. International Journal of Machine Learning and Cybernetics, ? . ?-?. ISSN 1868-8071

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Abstract

Feature selection is quite an important process in gene expression data analysis. Feature selection methods discard unimportant genes from several thousands of genes for finding important genes or pathways for the target biological phenomenon like cancer. The obtained gene subset is used for statistical analysis for prediction such as survival as well as functional analysis for understanding biological characteristics. In this paper we propose a null space based feature selection method for gene expression data in terms of supervised classification. The proposed method discards the redundant genes by applying the information of null space of scatter matrices. We derive the method theoretically and demonstrate its effectiveness on several DNA gene expression datasets. The method is easy to implement and computationally efficient.

Item Type:	Journal Article
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
Depositing User:	Ms Shalni Sanjana
Date Deposited:	23 Jul 2012 08:52
Last Modified:	18 Jan 2017 03:26
URI:	https://repository.usp.ac.fj/id/eprint/4962

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