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Attribute Coding for the Rough Set Theory Based Rule Simplications by Using the Particle Swarm Optimization Algorithm

  • Jieh-Ren Chang
  • Yow-Hao Jheng
  • Chi-Hsiang Lo
  • Betty Chang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 10)

Abstract

The attribute coding approach has been used in the Rough Set Theory (RST) based classification problems. The attribute coding defined ranges of the attribute values as multi-thresholds. If attribute values can be defined as appropriate values, the appropriate number of rules will be generated. The attribute coding for the RST based rule derivations significantly reduces unnecessary rules and simplifies the classification results. Therefore, how the appropriate attribute values can be defined will be very critical for rule derivations by using the RST. In this study, the authors intend to introduce the particle swarm optimization (PSO) algorithm to adjust the attribute setting scopes as an optimization problem to derive the most appropriate attribute values in a complex information system. Finally, the efficiency of the proposed method will be benchmarked with other algorithms by using the Fisher’s iris data set. Based on the benchmark results, the simpler rules can be generated and better classification performance can be achieved by using the PSO based attribute coding method.

Keywords

Particle Swarm Optimization (PSO) Rough Set Theory (RST) Attribute Coding optimization 

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References

  1. 1.
    Aha, D.W., Kibler, D.: Detecting and removing noisy instances from concept de-scriptions, Tecnical Report, University of California, Irvine, 88–12 (1989)Google Scholar
  2. 2.
    Chang, J.R., Jheng, Y.H.: Optimization of α-cut Value by using Genetic Algorithm for Fuzzy-based rules extraction. In: The 18th National Conference on Fuzzy and Its Applications, pp. 678–683 (2010)Google Scholar
  3. 3.
    Dasarathy, B.V.: Nosing around the neighborhood: A new system structure and classification rule for recognition in partially exposed environments. PAMI 2-1, 67–71 (1980)CrossRefGoogle Scholar
  4. 4.
    Dimitras, A.I., Slowinski, R., Susmaga, R., Zopounidis, C.: Business failure pre-diction using rough sets. European Journal of Operational Research 114(2), 263–280 (1999)zbMATHCrossRefGoogle Scholar
  5. 5.
    Hirsh, H.: Incremental version-space merging: a general framework for concept learning, Ph.D. Thesis, Stanford University (1990)Google Scholar
  6. 6.
    Hong, T.P., Chen, J.B.: Finding relevant attributes and membership functions. Fuzzy Sets and Systems 103(3), 389–404 (1999)CrossRefGoogle Scholar
  7. 7.
    Jackson, A.G., Leclair, S.R., Ohme, M.C., Ziarko, W., Kamhwi, H.A.: Rough sets applied to materials data. ACTA Material 44(11), 4475–4484 (1996)CrossRefGoogle Scholar
  8. 8.
    Jang, J.R., Hung, J.T.: Rough set theory inference to study pavement maintenance, National Science Council Research Project. Mingshin University of Science and Technology, Hsinchu, Taiwan (2005)Google Scholar
  9. 9.
    Jia, G., Zhang, W.: Using PSO to Reliability Analysis of PC Pipe Pile. In: The 3rd International Symposium on Computational Intelligence, vol. 1, pp. 68– 71 (2010)Google Scholar
  10. 10.
    Jin, J., Wang, Y., Wang, Q., Yang, B.Q.: The VNP-PSO method for medi-cal image registration.In: The 29th Chinese Control Conference, pp. 5203–5205 (2010)Google Scholar
  11. 11.
    Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceeding of the IEEE International Conference on Neural Networks, pp. 12–13 (1995)Google Scholar
  12. 12.
    Khamsawang, S., Wannakarn, P., Jiriwibhakorn, S.: Hybrid PSO-DE for solving the economic dispatch problem with generator constraints. In: 2010 2nd International Conference on Computer and Automation Engineering, vol. 5, pp. 135–139 (2010)Google Scholar
  13. 13.
    Kim, D.J., Ferrin, D.L., RaghavRao, H.: A study of the effect of consumer trust on consumer expectations and satisfaction: The Korean experience. In: Proceedings of the 5th International Conference on Electronic Commerce, Pittsburgh. ACM International Conference Proceeding Series, pp. 310–315 (2003)Google Scholar
  14. 14.
    Kondo, Y., Phimmasone, V., Ono, Y., Miyatake, M.: Verification of efficacy of PSO-based MPPT for photovoltaics. In: 2010 International Conference on Electrical Machines and Systems, pp. 593–596 (2010)Google Scholar
  15. 15.
    Pawlak, Z.: Rough sets. International Journal of Parallel Programming 11(5), 341–356 (1982)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishing, Dordrecht (1991)zbMATHGoogle Scholar
  17. 17.
    Quinlan, J.R., Compton, P.J., Horn, K.A., Lazarus, L.A.: Inductive knowledge ac-quisition: A case study. In: Quinlan, J.R. (ed.) Applications of Expert systems, Addison-Wesley, Wokingham (1987)Google Scholar
  18. 18.
    Shyng, J.Y., Wang, F.K., Tzeng, G.H., Wu, K.S.: Rough Set Theory in analyzing the attributes of combination values for the insurance market. Expert Systems with Applications 32(1), 56–64 (2007)CrossRefGoogle Scholar
  19. 19.
    Walczak, B., Massart, D.L.: Tutorial: Rough sets theory. Chemometrics and Intelligent Laboratory Systems 47, 1–17 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jieh-Ren Chang
    • 1
  • Yow-Hao Jheng
    • 2
  • Chi-Hsiang Lo
    • 3
  • Betty Chang
    • 4
  1. 1.Department of Electronic EngineeringNational Ilan UniversityI-LanR.O.C
  2. 2.Department of Business and Entrepreneurial AdministrationKainan UniversityI-LanR.O.C
  3. 3.Institute of Management of TechnologyNational Chiao Tung UniversityI-LanR.O.C
  4. 4.Graduate Institute of Architecture and Sustainable PlanningNational Ilan UniversityI-LanR.O.C

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