Ensemble Learning Classifier System and Compact Ruleset

Abstract

The aim of this paper is twofold, to improve the generalization ability, and to improve the readability of learning classifier system. Firstly, an ensemble architecture of LCS (LCSE) is described in order to improve the generalization ability of the original LCS. Secondly, an algorithm is presented for compacting the final classifier population set in order to improve the readability of LCSE, which is an amendatory version of CRA brought by Wilson. Some test experiments are conducted based on the benchmark data sets of UCI repository. The experimental results show that LCSE has better generalization ability than single LCS, decision tree, neural network and their bagging methods. Comparing with the original population rulesets, compact rulesets have readily interpretable knowledge like decision tree, whereas decrease the prediction precision lightly.