Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels |
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Authors: | Luciano S??nchez In??s Couso |
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Affiliation: | (1) Computer Science Department, University of Oviedo, Campus de Viesques, 33071 Gij?n, Asturias, Spain;(2) Statistics Department, Facultad de Ciencias, University of Oviedo, 33071 Oviedo, Asturias, Spain |
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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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Keywords: | |
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