Mining Imbalanced Data with Learning Classifier Systems

This chapter investigates the capabilities of XCS for mining imbalanced datasets. Initial experiments show that, for moderate and high class imbalances, XCS tends to evolve a large proportion of overgeneral classifiers. Theoretical analyses are developed, deriving an imbalance bound up to which XCS should be able to differentiate between accurate and overgeneral classifiers. Some relevant parameters that have to be properly configured to satisfy the bound for high class imbalances are detected. Configuration guidelines are provided, and an algorithm that automatically tunes these XCS’s parameters is presented. Finally, XCS is tested on a large set of real-world problems, appearing to be highly competitive to some of the most well-known machine learning techniques.