A cost-sensitive learning algorithm for fuzzy rule-based classifiers

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Document typeArticle
Defense date2004
PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
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is licensed under a Creative Commons license
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Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Designing 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
ISSN1134-5632
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