Abstract
Fuzzy memberships can be understood as coverage functions of random sets. This interpretation makes sense in the context of fuzzy rule learning: a random-sets-based semantic of the linguistic labels is compatible with the use of fuzzy statistics for obtaining knowledge bases from data. In particular, in this paper we formulate the learning of a fuzzy-rule-based classifier as a problem of statistical inference. We propose to learn rules by maximizing the likelihood of the classifier. Furthermore, we have extended this methodology to interval-censored data, and propose to use upper and lower bounds of the likelihood to evolve rule bases. Combining descent algorithms and a co-evolutionary scheme, we are able to obtain rule-based classifiers from imprecise data sets, and can also identify the conflictive instances in the training set: those that contribute the most to the indetermination of the likelihood of the model.
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This work was supported by the Spanish Ministry of Science and Innovation, under grants TIN2008-06681-C06-04 and TIN2007-67418-C03-03.
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Sánchez, L., Couso, I. Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels. Soft Comput 15, 1945–1957 (2011). https://doi.org/10.1007/s00500-010-0627-6
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DOI: https://doi.org/10.1007/s00500-010-0627-6