Handling Over-fitting and Class-imbalance Jointly in Pittsburgh LCS - Karmaker Santu, Shubhra Kanti;Islam, Md. Monirul
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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 algorithms.…mehr

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Produktbeschreibung
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.
Autorenporträt
Shubhra is a third Year PhD student at University of Illinois Urbana-Champaign. His research interest lies at the intersection of information retrieval and text mining. He also worked as research intern at Microsoft Research, Yahoo research and WalmartLabs.