Revistes Catalanes amb Accés Obert (RACO)

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

Sebastian Beck, Ralf Mikut, Jens Jäkel

Resum


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

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