By Topic

Comparison of Search Ability between Genetic Fuzzy Rule Selection and Fuzzy Genetics-Based Machine Learning

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

3 Author(s)
Nojima, Yusuke ; Dept. of Comput. Sci. & Intelligent Syst., Osaka Prefecture Univ. ; Ishibuchi, H. ; Kuwajima, I.

We developed two GA-based schemes for the design of fuzzy rule-based classification systems. One is genetic rule selection and the other is genetics-based machine learning (GBML). In our genetic rule selection scheme, first a large number of promising fuzzy rules are extracted from numerical data in a heuristic manner as candidate rules. Then a genetic algorithm is used to select a small number of fuzzy rules. A rule set is represented by a binary string whose length is equal to the number of candidate rules. On the other hand, a fuzzy rule is denoted by its antecedent fuzzy sets as an integer substring in our GBML scheme. A rule set is represented by a concatenated integer string. In this paper, we compare these two schemes in terms of their search ability to efficiently find compact fuzzy rule-based classification systems with high accuracy. The main difference between these two schemes is that GBML has a huge search space consisting of all combinations of possible fuzzy rules while genetic rule selection has a much smaller search space with only candidate rules

Published in:

Evolving Fuzzy Systems, 2006 International Symposium on

Date of Conference:

Sept. 2006