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dc.contributor.authorBeck, Sebastian
dc.contributor.authorMikut, Ralf
dc.contributor.authorJäkel, Jens
dc.date.accessioned2007-10-05T09:45:50Z
dc.date.available2007-10-05T09:45:50Z
dc.date.issued2004
dc.identifier.issn1134-5632
dc.identifier.urihttp://hdl.handle.net/2099/3645
dc.description.abstractDesigning classifiers may follow different goals. Which goal to prefer among others depends on the given cost situation and the class distribution. For example, a classifier designed for best accuracy in terms of misclassifica- tions may fail when the cost of misclassification of one class is much higher than that of the other. This paper presents a decision-theoretic extension to make fuzzy rule generation cost-sensitive. Furthermore, it will be shown how interpretability aspects and the costs of feature acquisition can be ac- counted for during classifier design. Natural language text is used to explain the generated fuzzy rules and their design process
dc.format.extent179-195
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.relation.ispartofMathware & soft computing . 2004 Vol. 11 Núm. 3
dc.rightsReconeixement-NoComercial-CompartirIgual 3.0 Espanya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.otherFuzzy rules
dc.titleA cost-sensitive learning algorithm for fuzzy rule-based classifiers
dc.typeArticle
dc.subject.lemacIntel·ligència artificial
dc.subject.lemacAlgorismes computacionals
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.rights.accessOpen Access


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