Rough set and teaching learning based optimization technique for optimal features selection
- 171 Downloads
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 with Teaching learning based optimization (TLBO). The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) and the empirical results reveal that the proposed approach could be used for feature selection as this performs better in terms of finding optimal features and doing so in quick time.
Keywordsfeature selection rough set TLBO
Unable to display preview. Download preview PDF.
- Bazan J., Nguyen H.S., Nguyen S.H., Synak P., Wroblewski J., Rough set algorithms in classification problem, 2000Google Scholar
- Benxian Y., Weihong Y., Ajith A., Hongbo L., A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization, Springer-LNCS 4527, 397–406, 2007Google Scholar
- Bjorvand A.T., Komorowski J., Practical Applications of Genetic Algorithms for Efficient Reduct Computation, Wissenschaft & Technik Verlag, 4, 601–606, 1997Google Scholar
- Emilyn J.J., Ramar K., Rough Set Based Clustering Of Gene Expression Data: A Survey, Int. J. Eng. Sci. Technol., 2, 7160–7164, 2010Google Scholar
- Hu K., Lu Y.C., Shi C.Y., Feature ranking in rough sets, AI Commun., 16, 41–50, 2003Google Scholar
- Krishnanand K.R., Panigrahi B.K., Rout P.K., Mohapatra A., Application of Multi-Objective Teaching-Learning- Based Algorithm to an Economic Load Dispatch Problem with Incommensurable Objectives. Swarm, Evolutionary, and Memetic Computing, Lect. Notes Comput. Sci., 7076, 697–705, 2011Google Scholar
- Satapathy S.C., Naik A., Hybridization of Rough Set and Differential Evolution Technique for Optimal Features Selection, Springer-AISC, 132, 453–460, 2012Google Scholar
- Satapathy S.C., Naik A., Data Clustering Based on Teaching-Learning-Based Optimization. Swarm, Evolutionary, and Memetic Computing, Lect. Notes Comput. Sci., 7077, 2011Google Scholar
- Skowron A., Rauszer C., The discernibility matrices and functions in information systems. Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht, 311–362, 1992Google Scholar
- Thangavel K., Shen Q., Pethalakshmi A., Application of Clustering for Feature Selection Based on Rough Set Theory Approach, AIML J, 6, 2006Google Scholar
- Wang G.Y. Zhao J., Theoretical Study on Attribute Reduction of Rough Set Theory: Comparison of Algebra and Information Views, In: Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004Google Scholar