Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning |
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Authors: | José Antonio Sanz,Alberto Ferná ndez |
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Affiliation: | a Department of Automatic and Computation, Public University of Navarra, Spain b Department of Computer Science, University of Jaén, P.O. Box 23071, Jaén, Spain c Department of Computer Science and Artificial Intelligence, CITIC-UGR (Research Center on Information and Communications Technology), University of Granada, Spain |
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Abstract: | 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. |
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Keywords: | Fuzzy rule-based classification systems Interval-valued fuzzy sets Tuning Genetic algorithms |
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