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Multi-agent Based Multi-knowledge Acquisition Method for Rough Set

  • Yang Liu
  • Guohua Bai
  • Boqin Feng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

The key problem in knowledge acquisition algorithm is how to deal with large-scale datasets and extract small number of compact rules. In recent years, several approaches to distributed data mining have been developed, but only a few of them benefit rough set based knowledge acquisition methods. This paper is intended to combine multi-agent technology into rough set based knowledge acquisition method. We briefly review the multi-knowledge acquisition algorithm, and propose a novel approach of distributed multi-knowledge acquisition method. Information system is decomposed into sub-systems by independent partition attribute set. Agent based knowledge acquisition tasks depend on universes of sub-systems, and the agent-oriented implementation is discussed. The main advantage of the method is that it is efficient on large-scale datasets and avoids generating excessive rules. Finally, the capabilities of our method are demonstrated on several datasets and results show that rules acquired are compact, having classification accuracy comparable to state-of-the-art methods.

Keywords

Attribute reduction Multi-agent technology Knowledge acquisition Classification accuracy 

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References

  1. 1.
    Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)CrossRefMathSciNetzbMATHGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht, Boston, London (1991)zbMATHGoogle Scholar
  3. 3.
    Wang, G.Y., Liu, F.: The inconsistency in rough set based rule generation. In: Rough Sets and Current Trends in Computing, pp. 370–377 (2000)Google Scholar
  4. 4.
    Yasdi, R.: Combining rough sets learning-method and neural learning-method to deal with uncertain and imprecise information. Neuro-Computing 7(1), 61–84 (1995)zbMATHGoogle Scholar
  5. 5.
    Wu, Q.: Bell, David: 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)CrossRefGoogle Scholar
  6. 6.
    Hang, X.S., Dai, H.: An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 153–165. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Klusch, M., Lodi, S., Moro, G.: Issues of agent-based distributed data mining. In: International Conference on Autonomous Agents, pp. 1034–1035 (2003)Google Scholar
  8. 8.
    Wooldridge, M.J., Jennings, N.R.: Intelligent agent: theory and practice. Knowledge Engineering Review 10(2), 115–152 (1995)CrossRefGoogle Scholar
  9. 9.
    Pynadath, D.V., Tambe, M.: The communicative multiagent team decision problem: Analyzing teamwork theories and models. Journal of Artificial Intelligence Research, 389–423 (2002)Google Scholar
  10. 10.
    Yaskawa, S., Sakata, A.: The application of intelligent agent technology to simulation. Mathematical and Computer Modelling 37(9), 1083–1092 (2003)CrossRefGoogle Scholar
  11. 11.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences, 3–27 (2007)Google Scholar
  12. 12.
    Grzymala-Busse, J.W.: MLEM2 - Discretization during rule induction. Intelligent Information Systems, 499–508 (2003)Google Scholar
  13. 13.
    UCI machine learning repository, http://archive.ics.uci.edu/ml/
  14. 14.
    Tay, F.E., Shen, L.: A modified Chi2 algorithm for discretization. IEEE Transactions on Knowledge and Data Engineering 14(3), 666–670 (2002)CrossRefGoogle Scholar
  15. 15.
    Aggarwal, C.C., Parthasarathy, S.: Mining massively incomplete data sets by conceptual reconstruction. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 227–232 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yang Liu
    • 1
    • 2
  • Guohua Bai
    • 2
  • Boqin Feng
    • 1
  1. 1.Department of Computer Science and TechnologyXi’an Jiaotong UniversityXi’anP.R. China
  2. 2.School of EngineeringBlekinge Institute of TechnologyRonnebySweden

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