Attribute Reduction in Decision-Theoretic Rough Set Models Using Genetic Algorithm

  • Srilatha Chebrolu
  • Sriram G. Sanjeevi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7076)


Real world data may contain inconsistencies, uncertainty and noise. Rough set model is a mathematical methodology in data analysis to deal with inconsistent and imperfect knowledge. Various probabilistic approaches to rough set model are proposed. Decision-theoretic rough set model (DTRSM) is one of the probabilistic approaches to rough set model. This paper proposes an attribute reduction algorithm in DTRSM, through region preservation. Attribute reduction is the process of identifying and removing redundant and irrelevant attributes from huge data sets, reducing its volume. The reduced data set can be much more effectively analyzed. Attribute reduction in DTRSM through region preservation is an optimization problem, thus Genetic Algorithm (GA) is used to achieve this optimization. Experiment results on discrete data sets are compared with local optimization approach based on discernibility matrix method and has been shown that GA can be effectively and efficiently used to achieve global minimal reduct.


Attribute Reduction Decision-Theoretic Rough Set Model Genetic Algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Beynon, M.: Reducts within the variable precision rough sets model: A further investigation. European Journal of Operational Research, 592–605 (2001)Google Scholar
  2. 2.
    Dun, L., Huaxiong, L., Xianzhong, Z.: Two decade’s research on decision-theoretic rough sets. In: ICCI 2010, pp. 968–973 (2010)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Pearson Education (2009)Google Scholar
  4. 4.
    Lin, L., Guang-hua, X.: Reduction of rough set attribute based on immune clone selection. Front. Mech. Eng. 4, 413–417 (2006)Google Scholar
  5. 5.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough sets. Communications of the ACM 8, 85–99 (1995)Google Scholar
  6. 6.
    Skowron, A., Grzymalla-Busse, J.: From rough set theory to evidence theory. In: Advances in the Dempster-Shafer Theory of Evidence. Wiley, New York (1994)Google Scholar
  7. 7.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Intelligent Decision Support Handbook of Applications and Advance of the Rough Sets Theory (1992)Google Scholar
  8. 8.
    Slezak, D.: Rough sets and bayes factor. T. Rough Sets, 202–229 (2005)Google Scholar
  9. 9.
    Yao, Y.: Probabilistic approaches to rough sets. Expert Systems 20, 287–297 (2003)CrossRefGoogle Scholar
  10. 10.
    Yao, Y.: Decision-Theoretic Rough Set Models. In: Yao, J., et al. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    Yao, Y.: Decision-theoretic rough set models (dtrsm). Computer Science 35(8A), 7–8 (2008)Google Scholar
  12. 12.
    Yao, Y.: Probabilistic rough set approximations. Int. J. Approx. Reasoning, 255–271 (2008)Google Scholar
  13. 13.
    Yao, Y.: Three-Way Decision: An Interpretation of Rules in Rough Set Theory. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 642–649. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Yao, Y.: Two semantic issues in a probabilistic rough set model. Fundamenta Informaticae, 1–17 (2010)Google Scholar
  15. 15.
    Yao, Y., Zhao, Y.: Attribute reduction in decision-theoretic rough set models. Inf. Sci., 3356–3373 (2008)Google Scholar
  16. 16.
    Zhao, Y., Wong, S.K.M., Yao, Y.: A Note on Attribute Reduction in the Decision-Theoretic Rough Set Model. In: Peters, J.F., et al. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 260–275. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci., 39–59 (1993)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Srilatha Chebrolu
    • 1
  • Sriram G. Sanjeevi
    • 1
  1. 1.Department of Computer Science and EngineeringNIT WarangalIndia

Personalised recommendations