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

• Autores: Sebastian Beck, Ralf Mikut, Jens Jäkel
• Localización: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology, ISSN-e 1134-5632, Vol. 11, Nº. 3, 2004, págs. 179-195
• Idioma: inglés
• Títulos paralelos:
  • Un algoritmo de aprendizaje sensible al coste para clasificadores basados en reglas difusas
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• Resumen

  • 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.