Towards better generalization in Pittsburgh learning classifier systems

Generalization ability of a classifier is an important issue for any classification task. This paper proposes a new evolutionary system, i.e., EDARIC, based on the Pittsburgh approach for evolutionary machine learning and classification. The new system uses a destructive approach that starts with large-sized rules and gradually decreases the sizes as evolution progresses. Unlike most previous works, EDARIC adopts an intelligent deletion mechanism, evolves a separate population for each class of a given problem and uses an ensemble system to classify unknown instances. These features help in avoiding over-fitting and class-imbalance problems, which are beneficial for improving generalization ability of a classification system. EDARIC also applies a rule post-processing step to exempt the evolution phase from the burden of tuning a large number of parameters. Experimental results on various benchmark classification problems reveal that EDARIC has better generalization ability in case of both standard and imbalanced datasets compared to many existing algorithms in the literature.