Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation
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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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2015
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| Online Access: | https://eprints.nottingham.ac.uk/33371/ |
| _version_ | 1848794615799349248 |
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| author | Navarro, Javier Wagner, Christian Aickelin, Uwe |
| author_facet | Navarro, Javier Wagner, Christian Aickelin, Uwe |
| author_sort | Navarro, Javier |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:19:01Z |
| format | Conference or Workshop Item |
| id | nottingham-33371 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:19:01Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-333712020-05-04T17:27:20Z https://eprints.nottingham.ac.uk/33371/ Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation Navarro, Javier Wagner, Christian Aickelin, Uwe 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. 2015-12-10 Conference or Workshop Item PeerReviewed Navarro, Javier, Wagner, Christian and Aickelin, Uwe (2015) Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation. In: 2015 IEEE Symposium Series on Computational Intelligence, 7-10 Dec 2015, Cape Town, South Africa. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7376830 |
| spellingShingle | Navarro, Javier Wagner, Christian Aickelin, Uwe Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title | Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title_full | Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title_fullStr | Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title_full_unstemmed | Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title_short | Applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| title_sort | applying interval type-2 fuzzy rule based classifiers through a cluster-based class representation |
| url | https://eprints.nottingham.ac.uk/33371/ https://eprints.nottingham.ac.uk/33371/ |