Towards final rule set reduction in XCS: a fuzzy representation approach

Authors:
Farzaneh Shoeleh;Ali Hamzeh;Sattar Hashemi
Affiliations:
School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran;School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

Generalization is the most challenging issue in XCS research area. One of the main components of XCS managing to remedy this issue is knowledge representation. In this paper, a knowledge representation based on fuzzy membership function offering certain and vague regions is described. We extend the Michigan learning classifier system using this approach to be improved in terms of both performance and interpretability. The contribution of this paper is three-folds: 1) updating main parameters of classifiers based on their certainty factor in matching of incoming data, 2) enhancing essential components of XCS to be compatible with such fuzzy representation schema and 3) proposing a novel rule set reduction method named Reduction based on Least Reward Prediction (RLRP) to improve the interpretability of the evolved model. Furthermore, an inference methodology which is compatible with RLRP is suggested to maintain the similar performance. The obtained results are promising due to the effectiveness of proposed method in dealing with real world problems. Furthermore, the proposed reduction method can upgrade the interpretability of final rule set by boiling its size down by 94% on average while slightly degrading the prediction accuracy.