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Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation

Navarro, Javier; Wagner, Christian; Aickelin, Uwe

Authors

Javier Navarro psxfjn@nottingham.ac.uk

Uwe Aickelin uwe.aickelin@nottingham.ac.uk



Abstract

Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i.e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules. This paper builds on prior work for interval type-2 fuzzy set based FRBCs where the fuzzy sets and rules of the classifier are generated using an initial clustering stage. By introducing Subtractive Clustering in order to identify multiple cluster prototypes, the proposed approach has the potential to deliver improved classification performance while maintaining good interpretability, i.e. without resulting in an excessive number of rules. The paper provides a detailed overview of the proposed FRBC framework, followed by a series of exploratory experiments on both linearly and non-linearly separable datasets, comparing results to existing rule-based and SVM approaches. Overall, initial results indicate that the approach enables comparable classification performance to non rule-based classifiers such as SVM, while often achieving this with a very small number of rules.

Publication Date Dec 10, 2015
Peer Reviewed Peer Reviewed
APA6 Citation Navarro, J., Wagner, C., & Aickelin, U. (2015). Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation
Publisher URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376830
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 : 8-10 December 2015, Cape Town, South Africa. Piscataway, N.J. : IEEE, 2015, pp. 1816-1823. doi:10.1109/SSCI.2015.253 ISBN: 9781479975617
©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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