learning classifier system ensemble for data mining
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.
learning classifier system ensemble