A combination-of-tools method for learning interpretable fuzzy rule-based classifiers from support vector machines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A new approach is proposed for the data-based identificar tion of transparent fuzzy rule-based classifiers. It is observed that fuzzy rule-based classifiers work in a similar manner as kernel function-based support vector machines (SVMs) since both model the input space by nonlinearly maps into a feature space where the decision can be easily made. Accordingly, trained SVM can be used for the construction of fuzzy rule-based classifiers. However, the transformed SVM does not automatically result in an interpretable fuzzy model because the SVM results in a complex rule-base, where the number of rules is approximately 40-60% of the number of the training data. Hence, reduction of the SVM-initialized classifier is an essential task. For this purpose, a three-step reduction algorithm is developed based on the combination of previously published model reduction techniques. In the first step, the identification of the SVM is followed by the application of the Reduced Set method to decrease the number of kernel functions. The reduced SVM is then transformed into a fuzzy rule-based classifier. The interpretability of a fuzzy model highly depends on the distribution of the membership functions. Hence, the second reduction step is achieved by merging similar fuzzy sets based on a similarity measure. Finally, in the third step, an orthogonal least-squares method is used to reduce the number of rules and re-estimate the consequent parameters of the fuzzy rule-based classifier. The proposed approach is applied for the Wisconsin Breast Cancer, Iris and Wine classification problems to compare its performance to other methods.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings
PublisherSpringer Verlag
Pages477-486
Number of pages10
ISBN (Print)9783540772255
DOIs
Publication statusPublished - 2007
Event8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom
Duration: Dec 16 2007Dec 19 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4881 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
CountryUnited Kingdom
CityBirmingham
Period12/16/0712/19/07

Keywords

  • Classification
  • Fuzzy classifier
  • Model reduction
  • Support vector machine

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'A combination-of-tools method for learning interpretable fuzzy rule-based classifiers from support vector machines'. Together they form a unique fingerprint.

  • Cite this

    Kenesei, T., Roubos, J. A., & Abonyi, J. (2007). A combination-of-tools method for learning interpretable fuzzy rule-based classifiers from support vector machines. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings (pp. 477-486). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_49