A cost-sensitive learning algorithm for fuzzy rule-based classifiers
Year: 2004
Type: article
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 misclassifications 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 accounted for during classifier design. Natural language text is used to explain the generated fuzzy rules and their design process. 1
Authors: Sebastian Beck, Ralf Mikut, Jens Jäkel
Institution: Karlsruhe Institute of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2145003766