Cart (Loading....) | Create Account
Close category search window
 

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chen, C.L.P. ; Dept. of Comput. Sci. & Eng., Wright State Univ., Dayton, OH, USA ; Lu, Y.

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

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:27 ,  Issue: 1 )

Date of Publication:

Feb 1997

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.