Rough set and teaching learning based optimization technique for optimal features selection

Research Article
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Abstract

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.

Keywords

feature selection rough set TLBO 

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References

  1. [1]
    Banerjee M., Mitra S., Anand A., Feature Selection Using Rough Sers, Stud. Studies Comput. Intell., 16, 3–20, 2006CrossRefGoogle Scholar
  2. [2]
    Bazan J., Nguyen H.S., Nguyen S.H., Synak P., Wroblewski J., Rough set algorithms in classification problem, 2000Google Scholar
  3. [3]
    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
  4. [4]
    Bjorvand A.T., Komorowski J., Practical Applications of Genetic Algorithms for Efficient Reduct Computation, Wissenschaft & Technik Verlag, 4, 601–606, 1997Google Scholar
  5. [5]
    Chouchoulas, A., Shen, Q., (2001) Rough set-aided keyword reduction for text Categorization. Appl. Artif. Intell., 15, 843–873CrossRefGoogle Scholar
  6. [6]
    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
  7. [7]
    Hu K., Lu Y.C., Shi C.Y., Feature ranking in rough sets, AI Commun., 16, 41–50, 2003Google Scholar
  8. [8]
    Hu X., Cereone N., Learning in relational databases: A rough set approach. Computational Intelligence, 11, 323–337, 1995CrossRefGoogle Scholar
  9. [9]
    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
  10. [10]
    Pawlak Z. Rough Sets, Int. J Comput. Inf. Sci., 11, 341–356, 1982MathSciNetMATHCrossRefGoogle Scholar
  11. [11]
    Pawlak Z., Rough Sets and Intelligent Data Analysis, Inf. Sci., 147, 1–12, 2002MathSciNetMATHCrossRefGoogle Scholar
  12. [12]
    Pawlak Z., Rough Sets: Present State and The Future, Foundations of Computing and Decision Sciences, 18, 157–166, 1993MathSciNetMATHGoogle Scholar
  13. [13]
    Rao R.V., Savsani V.J., Vakharia D.P., Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems. Comput.-Aided Des., 43, 303–315, 2011CrossRefGoogle Scholar
  14. [14]
    Rao R.V., Savsani V.J., Vakharia D.P., Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems. Inf. Sci., 183, 1–15, 2012MathSciNetCrossRefGoogle Scholar
  15. [15]
    Rao R.V., Savsani V.J., Mechanical design optimization using advanced optimization techniques (Springer-Verlag London, UK, 2012)CrossRefGoogle Scholar
  16. [16]
    Satapathy S.C., Naik A., Hybridization of Rough Set and Differential Evolution Technique for Optimal Features Selection, Springer-AISC, 132, 453–460, 2012Google Scholar
  17. [17]
    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
  18. [18]
    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
  19. [19]
    Thangavel K., Shen Q., Pethalakshmi A., Application of Clustering for Feature Selection Based on Rough Set Theory Approach, AIML J, 6, 2006Google Scholar
  20. [20]
    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

Copyright information

© Versita Warsaw and Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.Suresh Chandra Satapathy ANITSVishakapatnamIndia
  2. 2.MITSRayagadaIndia
  3. 3.Centurion University of Technology and Management (CUTM)ParalakhemundiIndia

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