Improving the Performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets and Genetic Amplitude Tuning
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Fuzzy rule based classification systemsIntervalValued Fuzzy SetsTuningGenetic algorithms
Antonio Sanz, J., Fernandez, A., Bustince, H., & Herrera, F. (2010). Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning. Information Sciences, 180(19), 3674-3685. doi:10.1016/j.ins.2010.06.018
SponsorshipSpanish Government TIN2008-06681-C06-01 TIN2007-65981
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 IntervalValued 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.