A cost-sensitive learning algorithm for fuzzy rule-based classifiers. (English) Zbl 1105.68363

Summary: 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 misciassifications 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 is 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.


68T05 Learning and adaptive systems in artificial intelligence
Full Text: EuDML