bounding xcs's parameters for unbalanced datasets
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.
class imbalance evolutionary computation machine learning genetic algorithms learning classifier systems