FUZZ: a fuzzy-based concept formation system that integrates human categorization and numerical clustering

Recently, psychologists proposed the prototype theory of concept representation, in which a concept is organized around a best example or so-called prototype. Most proponents of the prototype theory conceive that objects may fall in a concept to some degree rather than the all-or-none membership in the classical theory. Fuzzy-set theory is compatible with the basic premises of the prototype theory of concept representation. Concept formation is defined as a machine learning task that captures concepts through categorizing the observation of objects and also uses them in classifying future experiences. A reasonable computational model of concept formation must reflect the characteristics of human concept learning and categorization. In this paper, the design and implementation of a fuzzy-set based concept formation system (FUZZ) is presented. The main feature of the FUZZ is that the concept hierarchy is nondisjoint, in which an instance may belong to two categories in different memberships. An information-theoretic evaluation measure called category-binding to direct-searches in the FUZZ is proposed. The learning and classification algorithms of the FUZZ are also given. In order to examine FUZZ's behavior, the results of some experiments are examined.