By Topic

Rule Based Classifier Generation Using Symbiotic Evolutionary Algorithm

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

4 Author(s)
Halavati, R. ; Sharif Univ. of Technol., Tehran ; Shouraki, S.B. ; Esfandiar, P. ; Lotfi, S.

Genetic algorithms are vastly used in development of rule based classifier systems in data mining. In such tasks, the rule base is usually a set oflf-Then rules and the rules are developed using an evolutionary trait. GA is usually a good solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. This paper presents a novel algorithm for rule base generation based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. The new algorithm is compared with genetic algorithm on some globally used benchmarks and it is shown that it can either find better classification results from that of GA, or finds similar results with much less computation time.

Published in:

Tools with Artificial Intelligence, 2007. ICTAI 2007. 19th IEEE International Conference on  (Volume:1 )

Date of Conference:

29-31 Oct. 2007