Abstract
Gene selection, a key procedure of the discriminant analysis of microarray data, is to select the most informative genes from the whole gene set. Rough set theory is a mathematical tool for further reducing redundancy. One limitation of rough set theory is the lack of effective methods for processing real-valued data. However, most of gene expression data sets are continuous. Discretization methods can result in information loss. This paper investigates an approach combining feature ranking together with feature selection based on tolerance rough set theory. Compared with gene selection algorithm based on rough set theory, the proposed method is more effective for selecting high discriminative genes in cancer classification task.
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© 2009 Springer-Verlag Berlin Heidelberg
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Jiao, N., Miao, D. (2009). An Efficient Gene Selection Algorithm Based on Tolerance Rough Set Theory. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_21
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DOI: https://doi.org/10.1007/978-3-642-10646-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
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