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

Navarro, Javier; Wagner, Christian; Aickelin, Uwe


Javier Navarro

Uwe Aickelin


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.


Navarro, J., Wagner, C., & Aickelin, U. (2015). Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation.

Conference Name 2015 IEEE Symposium Series on Computational Intelligence
End Date Dec 10, 2015
Acceptance Date Sep 9, 2015
Publication Date Dec 10, 2015
Deposit Date Jun 21, 2016
Publicly Available Date Jun 21, 2016
Peer Reviewed Peer Reviewed
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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
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