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
On Determination of Minimum Sample Size for Discovery of Temporal Gene Expression Patterns
IMSCCS '06 Proceedings of the First International Multi-Symposiums on Computer and Computational Sciences - Volume 1 (IMSCCS'06) - Volume 01
Smart crossover operator with multiple parents for a Pittsburgh learning classifier system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Automated global structure extraction for effective local building block processing in XCS
Evolutionary Computation
Computational Methods of Feature Selection (Chapman & Hall/Crc Data Mining and Knowledge Discovery Series)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing)
Classifier fitness based on accuracy
Evolutionary Computation
New Crossover Operator for Evolutionary Rule Discovery in XCS
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Machine Learning in Bioinformatics
Machine Learning in Bioinformatics
Analysis and improvement of the genetic discovery component of XCS
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
IEEE Transactions on Evolutionary Computation
A new combined filter-wrapper framework for gene subset selection with specialized genetic operators
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Exploiting expert knowledge in genetic programming for genome-wide genetic analysis
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Learning classifier system with average reward reinforcement learning
Knowledge-Based Systems
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XCS is a learning classifier system that combines a reinforcement learning scheme with evolutionary algorithms to evolve a population of classifiers in the form of condition-action rules. In this paper, we investigate the effectiveness of XCS in high-dimensional classification problems where the number of features greatly exceeds the number of data instances --- common characteristics of microarray gene expression classification tasks. We introduce a new guided rule discovery mechanisms for XCS, inspired by feature selection techniques commonly used in machine learning. The extracted feature quality information is used to bias the evolutionary operators. The performance of the proposed model is compared with the standard XCS model and a number of well-known machine learning algorithms using benchmark binary classification tasks and gene expression data sets. Experimental results suggests that the guided rule discovery mechanism is computationally efficient, and promotes the evolution of more accurate solutions. The proposed model performs significantly better than comparative algorithms when tackling high-dimensional classification problems.