Description

Generalization ability of a classi er is an important issue for any classification task. Two prominent problems affecting the generalization ability are over- tting and class-imbalance. This book presents a new evolutionary system, i.e., EDARIC, for rule induction and classi cation. 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.

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