Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning

Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of...

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Bibliographic Details
Published in:Information sciences 2010-10, Vol.180 (19), p.3674-3685
Main Authors: Sanz, José Antonio, Fernández, Alberto, Bustince, Humberto, Herrera, Francisco
Format: Article
Language:English
Subjects:
Algorithms
Classification
Fuzzy
Fuzzy logic
Fuzzy rule-based classification systems
Fuzzy set theory
Genetic algorithms
Interval-valued fuzzy sets
Knowledge bases (artificial intelligence)
Mathematical analysis
Mathematical models
Tuning
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Summary:Among the computational intelligence techniques employed to solve classification problems, Fuzzy Rule-Based Classification Systems (FRBCSs) are a popular tool because of their interpretable models based on linguistic variables, which are easier to understand for the experts or end-users. The aim of this paper is to enhance the performance of FRBCSs by extending the Knowledge Base with the application of the concept of Interval-Valued Fuzzy Sets (IVFSs). We consider a post-processing genetic tuning step that adjusts the amplitude of the upper bound of the IVFS to contextualize the fuzzy partitions and to obtain a most accurate solution to the problem. We analyze the goodness of this approach using two basic and well-known fuzzy rule learning algorithms, the Chi et al.’s method and the fuzzy hybrid genetics-based machine learning algorithm. We show the improvement achieved by this model through an extensive empirical study with a large collection of data-sets.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2010.06.018