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)


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.


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


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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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