Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization

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115 Citations (Scopus)

Abstract

The data-driven identification of fuzzy rule-based classifiers for high-dimensional problems is addressed. A binary decision-tree-based initialization of fuzzy classifiers is proposed for the selection of the relevant features and effective initial partitioning of the input domains of the fuzzy system. Fuzzy classifiers have more flexible decision boundaries than decision trees (DTs) and can therefore be more parsimonious. Hence, the decision tree initialized fuzzy classifier is reduced in an iterative scheme by means of similarity-driven rule-reduction. To improve classification performance of the reduced fuzzy system, a genetic algorithm with a multiobjective criterion searching for both redundancy and accuracy is applied. The proposed approach is studied for (i) an artificial problem, (ii) the Wisconsin Breast Cancer classification problem, and (iii) a summary of results is given for a set of well-known classification problems available from the Internet: Iris, Ionospehere, Glass, Pima, and Wine data.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalInternational Journal of Approximate Reasoning
Volume32
Issue number1
DOIs
Publication statusPublished - Jan 2003

Keywords

  • Classification
  • Decision tree
  • Fuzzy classifier
  • Genetic algorithm
  • Model reduction

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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