Abstract
This article provides an overview of recent literature on some tasks of pattern recognition using rough sets and its hybridization with other soft computing paradigms. Rough set theory is an established tool for dealing with imprecision, noise, and uncertainty in data. In this article we will focus on two recent applications using rough sets; viz., feature selection in high dimensional gene expression data, and collaborative clustering. The experimental results demonstrate that the incorporation of rough set improves the performance of the system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic, Dordrecht (1992)
Wroblewski, J.: Finding minimal reducts using genetic algorithms. Technical Report 16/95, Warsaw Institute of Technology - Institute of Computer Science, Poland (1995)
Bjorvand, A.T.: ‘Rough Enough’ – A system supporting the rough sets approach. In: Proceedings of the Sixth Scandinavian Conference on Artificial Intelligence, Helsinki, Finland, pp. 290–291 (1997)
Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley, London (2001)
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37, 77–84 (1994)
Mitra, S., Acharya, T.: Data Mining: Multimedia, Soft Computing, and Bioinformatics. John Wiley, New York (2003)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
Lingras, P., West, C.: Interval set clustering of Web users with rough k-means. Technical Report No. 2002-002, Dept. of Mathematics and Computer Science, St. Mary’s University, Halifax, Canada (2002)
Pedrycz, W.: Collaborative fuzzy clustering. Pattern Recognition Letters 23, 1675–1686 (2002)
Deb, K., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research 5, 1205–1224 (2004)
Banerjee, M., Mitra, S., Banka, H.: Evolutionary-rough feature selection in gene expression data. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews (to appear)
Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, London (1974)
Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36, 795–805 (2006)
Mitra, S.: An evolutionary rough partitive clustering. Pattern Recognition Letters 25, 1439–1449 (2004)
Special Issue on Bioinformatics. IEEE Computer 35 (2002)
Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman, New York (1999)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this chapter
Cite this chapter
Mitra, S., Banka, H. (2007). Application of Rough Sets in Pattern Recognition. In: Peters, J.F., Skowron, A., Marek, V.W., Orłowska, E., Słowiński, R., Ziarko, W. (eds) Transactions on Rough Sets VII. Lecture Notes in Computer Science, vol 4400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71663-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-71663-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71662-4
Online ISBN: 978-3-540-71663-1
eBook Packages: Computer ScienceComputer Science (R0)