Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
What Is a Learning Classifier System?
Learning Classifier Systems, From Foundations to Applications
Get Real! XCS with Continuous-Valued Inputs
Learning Classifier Systems, From Foundations to Applications
An Algorithmic Description of XCS
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Two Views of Classifier Systems
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
DXCS: an XCS system for distributed data mining
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Collective behavior based hierarchical XCS
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
Cooperative co-evolution of GA-based classifiers based on input decomposition
Engineering Applications of Artificial Intelligence
Improving XCS Performance by Distribution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Machine Learning in Bioinformatics
Machine Learning in Bioinformatics
Toward a theory of generalization and learning in XCS
IEEE Transactions on Evolutionary Computation
PCA for improving the performance of XCSR in classification of high-dimensional problems
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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Learning classifier systems (LCSs) are a machine learning technique, which combine reinforcement learning and evolutionary algorithms to evolve a set of classifiers (or rules) for pattern classification tasks. Despite promising performance across a variety of data sets, the performance of LCS is often degraded when data sets of high dimensionality and relatively few instances are encountered, a common occurrence with gene expression data. In this paper, we propose a number of extensions to XCS, a widely used accuracy-based LCS, to tackle such problems. Our model, CoXCS, is a coevolutionary multi-population XCS. Isolated sub-populations evolve a set of classifiers based on a partitioning of the feature space in the data. Modifications to the base XCS framework are introduced including an algorithm to create the match set and a specialized crossover operator. Experimental results show that the accuracy of the proposed model is significantly better than other well-known classifiers when the ratio of data features to samples is extremely large.