Abstract
Dimensionality reduction of a feature set refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough set theory hybrid with Differential Evolution (DE) try to improve this. We call this method as RoughDE. The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
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References
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis 1(3), 131–156 (1997)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z.: Rough Sets: Present State and The Future. Foundations of Computing and Decision Sciences 18, 157–166 (1993)
Pawlak, Z.: Rough Sets and Intelligent Data Analysis. Information Sciences 147, 1–12 (2002)
Bonabeau, E., Dorigo, M., Theraulez, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press Inc., New York (1999)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht (1991)
Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.): Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56. Physica-Verlag, Heidelberg (2000)
Storn, R., Price, K.: Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Thangavel, K., Shen, Q., Pethalakshmi, A.: Application of Clustering for Feature Selection Based on Rough Set Theory Approach. AIML Journal 6(1) (January 2006)
Yue, B., Yao, W., Abraham, A., Liu, H.: A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2007. LNCS, vol. 4527, pp. 397–406. Springer, Heidelberg (2007)
Jeba Emilyn, J., Ramar, K.: Rough Set Based Clustering Of Gene Expression Data: A Survey. International Journal of Engineering Science and Technology 2(12), 7160–7164 (2010)
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Satapathy, S.C., Naik, A. (2012). Hybridization of Rough Set and Differential Evolution Technique for Optimal Features Selection. In: Satapathy, S.C., Avadhani, P.S., Abraham, A. (eds) Proceedings of the International Conference on Information Systems Design and Intelligent Applications 2012 (INDIA 2012) held in Visakhapatnam, India, January 2012. Advances in Intelligent and Soft Computing, vol 132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27443-5_52
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DOI: https://doi.org/10.1007/978-3-642-27443-5_52
Publisher Name: Springer, Berlin, Heidelberg
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