Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Bounding XCS's parameters for unbalanced datasets
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Classifier fitness based on accuracy
Evolutionary Computation
The class imbalance problem in UCS classifier system: a preliminary study
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Learning Classifier Systems: Looking Back and Glimpsing Ahead
Learning Classifier Systems
BRACID: a comprehensive approach to learning rules from imbalanced data
Journal of Intelligent Information Systems
Engineering Applications of Artificial Intelligence
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This paper analyzes the scalability of the population size required in XCS to maintain nichesthat are infrequently activated.Facetwise models have been developed to predict the effect of the imbalance ratio--ratio betweenthe number of instances of the majority class and the minority class that are sampled to XCS--on population initialization, andon the creation and deletion of classifiers of the minority class. While theoretical models show that, ideally, XCS scales linearly with the imbalance ratio, XCS with standard configuration scales exponentially. The causes that are potentially responsible for this deviation from the ideal scalability are also investigated. Specifically, the inheritance procedure of classifiers' parameters, mutation, and subsumption are analyzed, and improvements in XCS's mechanisms are proposed to effectively and efficiently handle imbalanced problems. Once the recommendations are incorporated to XCS, empirical results show that the population size in XCS indeed scales linearly with the imbalance ratio.