By Topic

Using accuracy-based learning classifier systems for imbalance datasets

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)
Udomthanapong, S. ; Dept. of Comput. Eng., King Mongkut''s Inst. of Technol., Bangkok ; Tamee, K. ; Pinngern, O.

XCS is one of the most powerful learning classifier systems. It combines reinforcement learning and genetic algorithm to create a set of rules representing the extracted knowledge from dataset. The main advantage of this system is to provide rule-based models that represent human-readable patterns. However, not too much public have yet been studied in imbalance dataset. In this paper, we propose a novel technique to develop XCS deal with imbalance dataset. The proposed technique uses adaptive perception rate for each rule to provide balance learning between major and minor class. The experiment show that the propose technique can classify all level of imbalance classes on the well-know Boolean logic benchmark task - multiplexer problem.

Published in:

Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 2008. ECTI-CON 2008. 5th International Conference on  (Volume:1 )

Date of Conference:

14-17 May 2008