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