LCSE: learning classifier system ensemble for incremental medical instances

Authors:
Yang Gao;Joshua Zhexue Huang;Hongqiang Rong;Da-qian Gu
Affiliations:
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China;E-business Technology Institute, The University of Hong Kong, China;Department of Computer Science, The University of Hong Kong, China;State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Venue:
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Year:
2007

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Abstract

This paper proposes LCSE, a learning classifier system ensemble, which is an extension to 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 after the learning module 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 simple plurality-vote method is used to combine the classification results of individual LCSs into a final decision. Experiments on some benchmark medical data sets from the UCI repository have shown that LCSE has better performance on incremental medical data learning and better generalization ability than the single LCS and other supervised learning methods.