Handling Over-fitting and Class-imbalance Jointly in Pittsburgh LCS

Generalization ability of a classifier is an important issue for any classification task. Two prominent problems affecting the generalization ability are over-fitting and class-imbalance. This book presents a new evolutionary system, i.e., EDARIC, for rule induction and classification. The evolutionary approach used in our new system is based on a destructive method that starts with large-sized rules and gradually decreases the sizes as evolution progresses. The experimental results show that our proposed evolutionary system obtains better generalization performance compared to the existing algorithms.