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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Rough Sets. International Journal of Information Computer Science 11(5), 341–356 (1982)
Jensen, R., Shen, Q.: Tolerance-Based and Fuzzy-Rough Feature Selection. In: Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ- IEEE 2007), pp. 877–882 (2007)
Parthalin, N.M., Shen, Q.: Exploring The Boundary Region of Tolerance Rough Sets for Feature Selection. Pattern Recognition 42, 655–667 (2009)
Miao, D.Q., Wang, J.: Information-Based Algorithm for Reduction of Knowledge. In: IEEE International Conference on Intelligent Processing Systems, pp. 1155–1158 (1997)
Wang, G.Y.: Rough Set Theory and Knowledge Acquisition. Xi’an Jiaotong University Press (2001) (in Chinese)
Li, D.F., Zhang, W.: Gene Selection Using Rough Set Theory. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 778–785. Springer, Heidelberg (2006)
Grzymala-Busse, J.W.: Discretization of Numerical Attributes. In: Klsgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 218–225. Oxford University Press, Oxford (2002)
Grzymala-Busse, J.W., Grzymala-Busse, W.J.: Handling Missing Attribute Values. In: Maimon, O., Rokach, L. (eds.) Handbook of Data Mining and Knowledge Discovery, pp. 37–57 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-10646-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
Online ISBN: 978-3-642-10646-0
eBook Packages: Computer ScienceComputer Science (R0)