Design of Fuzzy Rule-based Classifiers with Inhomogeneous Fuzzy Partitions

DOI

Bibliographic Information

Other Title
  • 不均等ファジィ集合を用いたファジィ識別器設計

Abstract

Discretization of continuous attributes is a key issue in classifier design from numerical data. In the fuzzy systems community, generating membership functions from numerical data has been an important research topic. Fuzzy genetics-based machine learning (GBML), which is one of the frequently-used techniques to design fuzzy rule-based classifiers, has often used uniform fuzzy partitions to generate initial membership functions. In this paper, we apply a class entropy measure to the discritization of numerical attiributes in order to generate inhomogeneous interval partitions. Nonuniform asymmetric membership functions are constructed from the generated interval partitions by introducing a parameter called "fuzzification grade" (0: interval, 1: full fuzzification). Through computational experiments, we examine the effects of the fuzzification grade on the accuracy of fuzzy rule-based classifiers. We also propose an ensemble classifier which has several fuzzy rule-based classifiers with different fuzzification grades.

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Details 詳細情報について

  • CRID
    1390282680650169088
  • NII Article ID
    130005480456
  • DOI
    10.14864/fss.30.0_196
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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