Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Data Mining and Knowledge Discovery
XCS and GALE: A Comparative Study of Two Learning Classifier Systems on Data Mining
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
For real! XCS with continuous-valued inputs
Evolutionary Computation
The effect of small disjuncts and class distribution on decision tree learning
The effect of small disjuncts and class distribution on decision tree learning
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
Rule-based evolutionary online learning systems: learning bounds, classification, and prediction
The class imbalance problem: A systematic study
Intelligent Data Analysis
Classifier fitness based on accuracy
Evolutionary Computation
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Data mining in learning classifier systems: comparing XCS with GAssist
IWLCS'03-05 Proceedings of the 2003-2005 international conference on Learning classifier systems
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
Evolutionary learning of hierarchical decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling XCS in class imbalances: population size and parameter settings
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Revisiting UCS: Description, Fitness Sharing, and Comparison with XCS
Learning Classifier Systems
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The class imbalance problem has been said recently to hinder the performance of learning systems. In fact, many of them are designed with the assumption of well-balanced datasets. But this commitment is not always true, since it is very common to find higher presence of one of the classes in real classification problems. The aim of this paper is to make a preliminary analysis on the effect of the class imbalance problem in learning classifier systems. Particularly we focus our study on UCS, a supervised version of XCS classifier system. We analyze UCS's behavior on unbalanced datasets and find that UCS is sensitive to high levels of class imbalance. We study strategies for dealing with class imbalances, acting either at the sampling level or at the classifier system's level.