A method of tumor classification based on wavelet packet transforms and neighborhood rough set

Authors:
Shan-Wen Zhang;De-Shuang Huang;Shu-Lin Wang
Affiliations:
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, P.O. Box 1130, Hefei, Anhui 230031, China
Venue:
Computers in Biology and Medicine
Year:
2010

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Abstract

Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.