The class imbalance problem in learning classifier systems: a preliminary study
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
The class imbalance problem: A systematic study
Intelligent Data Analysis
Classifier fitness based on accuracy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Modeling XCS in class imbalances: population size and parameter settings
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Coevolutionary bid-based genetic programming for problem decomposition in classification
Genetic Programming and Evolvable Machines
MATES '08 Proceedings of the 6th German conference on Multiagent System Technologies
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
Evolving Fuzzy Rules with UCS: Preliminary Results
Learning Classifier Systems
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
Intrusion detection with evolutionary learning classifier systems
Natural Computing: an international journal
Learning sensorimotor control structures with XCSF: redundancy exploitation and dynamic control
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolutionary undersampling for classification with imbalanced datasets: Proposals and taxonomy
Evolutionary Computation
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Facetwise analysis of XCS for problems with class imbalances
IEEE Transactions on Evolutionary Computation
Controlling a four degree of freedom arm in 3D using the XCSF learning classifier system
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
How XCS deals with rarities in domains with continuous attributes
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A combined approach to tackle imbalanced data sets
International Journal of Hybrid Intelligent Systems
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This paper analyzes the behavior of the XCS classifier system on imbalanced datasets. We show that XCS with standard parameter settings is quite robust to considerable class imbalances. For high class imbalances, XCS suffers from biases toward the majority class. We analyze XCS's behavior under such extreme imbalances and prove that appropriate parameter tuning improves significantly XCS's performance. Specifically, we counterbalance the imbalance ratio by equalizing the reproduction probabilities of the most occurring and least occurring niches. The study provides guidelines to tune XCS's parameters for unbalanced datasets, based on the dataset imbalance ratio. We propose a method to estimate the imbalance ratio during XCS's training and adapt XCS's parameters online.