Fuzzy Classification: An Overview

Fuzzy classification, fuzzy diagnosis, and fuzzy data analysis are — besides fuzzy control — the most important application areas of fuzzy logic. In this chapter four practical tasks are presented which can roughly be characterized as technical classification and diagnosis, fuzzy data analysis in chemical model creation, medical object recognition, and decision making support by a life insurance. Neural networks and analytical methods of classical statistics try to find explicitely a classifying function with the help of a sample. The development of knowledge-based systems has stimulated modern constructive approaches like IF-THEN-rules and causal networks. In order to deal with vague observations, vague relationships between features, and/or non-crip classification, these analytical and constructive methods were transfered from crisp numbers to fuzzy sets. The contributions of fuzzy sets to the four applications of this chapter are presented. Some general remarks on the applicability and limitations of fuzzy classification conclude this short introduction to fuzzy classification.