A Critical Review of Classifier Systems
Proceedings of the 3rd International Conference on Genetic Algorithms
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Learning classifier system ensemble for data mining
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier system ensemble and compact rule set
Connection Science - Evolutionary Learning and Optimisation
Mining comprehensible clustering rules with an evolutionary algorithm
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
A fast directed tree based neighborhood clustering algorithm for image segmentation
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Learning Classifier System (LCS) is an effective tool to solve classification problems. Clustering with XCS (accuracy-based LCS) is a novel approach proposed recently. In this paper, we revise the framework of XCS, and present a complete framework of clustering with XCS. XCS consists of two major modules: reinforcement learning and genetic algorithm. After the learning process, the learnt rules are always redundant and the large ruleset is incomprehensive. We adopt the revised compact rule algorithm to compress the ruleset, and propose a new rule merging algorithm to merge rules for generating genuine clustering results without knowing of the number of clusters. The experiment results on several complex structure datasets show that out approach performs well on challenging synthetic datasets.