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Learning Classifier Systems for Class Imbalance Problems
The document discusses learning classifier systems (LCS) for addressing class imbalance problems in datasets. It aims to enhance the applicability of LCS to knowledge discovery from real-world datasets that often exhibit class imbalance, where one class is represented by significantly fewer examples than other classes. The author proposes adapting parameters of the XCS learning classifier system, such as learning rate and genetic algorithm threshold, based on estimated class imbalance ratios within classifiers' niches in order to minimize bias towards majority classes and better handle small disjuncts representing minority classes.