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Soft Computing

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01.10.2011 | Focus

Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels

verfasst von: Luciano Sánchez, Inés Couso

Erschienen in: Soft Computing | Ausgabe 10/2011

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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.

Vorheriger Artikel An IVFS-based image segmentation methodology for rat gait analysis

Nächster Artikel HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers

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Titel: Obtaining fuzzy rules from interval-censored data with genetic algorithms and a random sets-based semantic of the linguistic labels
verfasst von: Luciano Sánchez
Inés Couso
Publikationsdatum: 01.10.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 10/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0627-6

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