Classifying Attributes with Game-Theoretic Rough Sets

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)

Abstract

Attributes may be categorized as core, reduct or non-reduct attributes when rough set theory is utilized for classification. These attribute types play different roles in feature selection algorithms. We introduce a game-theoretic rough set based method that formulates the classification of an attribute as a decision problem within a game. In particular, multiple measures representing importance levels for an attribute are incorporated into a unified framework to obtain an effective attribute classification mechanism. Demonstrative example suggests that the method may be efficient in classifying different types of attributes.

Keywords

Payoff Function Feature Selection Algorithm Core Attribute Attribute Importance Discernibility Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Yao, Y., Zhao, Y., Wang, J., Han, S.: A Model of User-oriented Reduct Construction for Machine Learning. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets VIII. LNCS, vol. 5084, pp. 332–351. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Yao, Y.Y., Zhao, Y.: Discernibility matrix simplification for constructing attribute reducts. Information Science 179(7), 867–882 (2009)MathSciNetMATHCrossRefGoogle Scholar
  3. 3.
    Yao, J.T., Zhang, M.: Feature Selection with Adjustable Criteria. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3641, pp. 204–213. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Wei, L., Li, H.-R., Zhang, W.-X.: Knowledge reduction based on the equivalence relations defined on attribute set and its power set. Information Science 177(15), 3178–3185 (2007)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Zhang, M., Xu, L.D., Zhang, W.-X., Li, H.-Z.: A rough set approach to knowledge reduction based on inclusion degree and evidence reasoning theory. Expert Systems 20(5), 298–304 (2003)CrossRefGoogle Scholar
  6. 6.
    Hu, Q., Liu, J., Yu, D.: Mixed feature selection based on granulation and approximation. Knowledge Based Systems 21(4), 294–304 (2008)CrossRefGoogle Scholar
  7. 7.
    Hu, X., Lin, T.Y., Han, J.: A new rough sets model based on database systems. Fundamenta Informaticae 59(2-3), 135–152 (2004)MathSciNetMATHGoogle Scholar
  8. 8.
    Ishii, N., Morioka, Y., Bao, Y., Tanaka, H.: Control of Variables in Reducts - kNN Classification with Confidence. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS, vol. 6884, pp. 98–107. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Strąkowski, T., Rybiński, H.: A New Approach to Distributed Algorithms for Reduct Calculation. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 365–378. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Liu, H., Abraham, A., Yue, B.: Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning II. SCI, vol. 263, pp. 445–466. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Wu, Q., Bell, D.A.: Multi-Knowledge Extraction and Application. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 274–278. Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Ziarko, W., Wong, S.K.M.: On optimal decision rules in decision table. Bulletin of Polish Academy of Sciences 33(11-12), 693–696 (1985)MathSciNetMATHGoogle Scholar
  13. 13.
    Wang, P.-C.: Highly scalable rough set reducts generation. Journal of Information Science and Engineering 23(4), 1281–1298 (2007)Google Scholar
  14. 14.
    Herbert, J.P., Yao, J.T.: Game-theoretic rough sets. Fundamenta Informaticae 108(3-4), 267–286 (2011)MathSciNetMATHGoogle Scholar
  15. 15.
    Yao, J.T., Herbert, J.P.: A game-theoretic perspective on rough set analysis. Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition) 20(3), 291–298 (2008)Google Scholar
  16. 16.
    Herbert, J.P., Yao, J.: Analysis of Data-Driven Parameters in Game-Theoretic Rough Sets. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 447–456. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  17. 17.
    von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press (1944)Google Scholar
  18. 18.
    Herbert, J.P., Yao, J.T.: A Game-Theoretic Approach to Competitive Learning in Self-Organizing Maps. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 129–138. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  19. 19.
    Azam, N., Yao, J.T.: Incorporating Game Theory in Feature Selection for Text Categorization. In: Kuznetsov, S.O., Ślęzak, D., Hepting, D.H., Mirkin, B.G. (eds.) RSFDGrC 2011. LNCS, vol. 6743, pp. 215–222. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Kol, G., Naor, M.: Cryptography and Game Theory: Designing Protocols for Exchanging Information. In: Canetti, R. (ed.) TCC 2008. LNCS, vol. 4948, pp. 320–339. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  21. 21.
    Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Basar, T.: Coalitional game theory for communication networks. IEEE Signal Processing Magazine 26(5)Google Scholar
  22. 22.
    Yao, Y.: Decision-Theoretic Rough Set Models. In: Yao, J., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślęzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 1–12. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  23. 23.
    Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 297–304. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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