Title :
A fuzzy-based concept formation system for categorization and numerical clustering
Author :
Chen, C. L Philip ; Lu, Yuan
Author_Institution :
Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA
Abstract :
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 non-disjoint, 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
Keywords :
fuzzy logic; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); FUZZ; categorization; category binding; classification algorithms; computational model; concept formation; concept hierarchy; concept representation; fuzzy-based concept formation system; fuzzy-set based concept formation system; fuzzy-set theory; information-theoretic evaluation measure; machine learning task; numerical clustering; prototype theory; Classification algorithms; Clustering algorithms; Contracts; Fuzzy systems; Learning systems; Radio access networks;
Conference_Titel :
Fuzzy Systems Symposium, 1996. Soft Computing in Intelligent Systems and Information Processing., Proceedings of the 1996 Asian
Conference_Location :
Kenting
Print_ISBN :
0-7803-3687-9
DOI :
10.1109/AFSS.1996.583632
