Learning classifier system ensemble for data mining

Authors:
Yang Gao;Joshua Zhexue Huang;Hongqiang Rong;Daqian Gu
Affiliations:
Nanjing University, Nanjing, China;The University of Hong Kong, Hong Kong, China;The University of Hong Kong, Hong Kong, China;Nanjing University, Nanjing
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

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Abstract

This paper proposes LCSE, a learning classifier system ensemble, which is an extension of the classical learning classifier system(LCS). The classical LCS includes two major modules, a genetic algorithm module used to facilitate rule discovery, and a reinforcement learning module used to adjust the strength of the corresponding rules while it receives the rewards from the environment. In LCSE we build a two-level ensemble architecture to enhance the generalization of LCS. In the first-level, new instances are first bootstrapped and sent to several LCSs for classification. Then, in the second-level, a plurality-vote method is used to combine the classification results of individual LCSs into a final decision. Experiments on some benchmark data sets from the UCI repository have shown that LCSE has better generalization ability than the single LCS and other supervised learning methods.