Abstract
The generic approach to cancer classification based on gene expression data is important for accurate cancer diagnosis, instead of using all genes in the dataset, we select a small gene subset out of thousands of genes for classification. Rough set theory is a tool for reducing redundancy in information systems, thus Application of Rough Set to gene selection is interesting. In this paper, a novel gene selection method called RMIMR is proposed for gene selection, which searches for the subset through maximum relevance and maximum positive interaction of genes. Compared with the classical methods based on statistics,information theory and regression, Our method leads to significantly improved classification in experiments on 4 gene expression datasets.
Supported by the National Key Research Program of China (No. 2003CSCA00200) and the National Key Lab Open Research Foundation (No. 2005C012).
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Li, D., Zhang, W. (2006). Gene Selection Using Rough Set Theory. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_113
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DOI: https://doi.org/10.1007/11795131_113
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