Attribute Reduction Based on Equivalence Classes with Multiple Decision Values in Rough Set

Conference paper
First Online:
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 219)

Abstract

For the attribute reduction problem of decision information systems, the concept of the equivalence class only including the condition attributes is introduced. The necessary condition of implementing attribute reduction and the attribute reduction method based on the equivalence classes with the multiple decision values are presented. After sorting the condition attributes by the cardinalities of the equivalence classes with the multiple decision value in ascending order, these ordered condition attributes are united one by one until the positive region of the united attribute subset is equal to the full region. Furthermore, if the attribute subset is independent and its indiscernibility relation is the same as the indiscernibility relation in original information system, then the subset is an attribute reduction of the information system. Finally, the experiment result demonstrates that our method is efficient.

Keywords

Attribute reduction Rough set Equivalence class Multiple decision values 
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References

  1. 1.
    Salamó M, López-Sánchez M (2011) Rough set based approaches to feature selection for case-based reasoning classifiers. Pattern Recogn Lett 32(15):280–292CrossRefGoogle Scholar
  2. 2.
    Das AK, Sil J (2011) An efficient classifier design integrating rough set and set oriented database operations. Appl Soft Comput 11(8):2279–2285Google Scholar
  3. 3.
    Skowron A (1995) Extracting laws from decision tables: a rough set approach. Comput Intell 11(27):371–388CrossRefMathSciNetGoogle Scholar
  4. 4.
    Shao MW, Zhang WX (2005) Dominance relation and rules in an incomplete ordered information system. Int J Intell Syst 20(14):13–27CrossRefMATHGoogle Scholar
  5. 5.
    Grzymala-Busse JW (1991) An algorithm for computing a single covering, vol 62(25). Kluwer Academic Publishers, Dordrecht, pp 347–351Google Scholar
  6. 6.
    Grzymala-Busse JW (1991) LERS–a system for learning from examples based on rough sets, vol 53(15). Kluwer Academic Publishers, Dordrecht, pp 643–647Google Scholar
  7. 7.
    Hu QH, Xie ZX, Yu DR (2007) Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn 40(17):3509–3521CrossRefMATHGoogle Scholar
  8. 8.
    Qian J, Miao DQ, Zhang ZH (2011) Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation. Int J Approximate Reasoning 52(16):212–230CrossRefMATHMathSciNetGoogle Scholar
  9. 9.
    Yang SZ, Ding H, Shi TL (1993) Diagnosis reasoning based on knowledge, vol 14(28). Tsinghua University Press, Beijing, pp 455–459Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.School of information Science and EngineeringHebei University of Science and TechnologyShijiazhuangChina
  2. 2.School of SciencesHebei University of Science and TechnologyShijiazhuangChina

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