论文信息 - Discovery of Significant Classification Rules from Incrementally Inducted Decision Tree Ensemble for Diagnosis of Disease

Discovery of Significant Classification Rules from Incrementally Inducted Decision Tree Ensemble for Diagnosis of Disease

Previous studies show that using significant classification rules to accomplish the classification task is suitable for bio-medical research. Discovery of many significant rules could be performed by using ensemble methods in decision tree induction. However, those traditional approaches are not useful for incremental task. In this paper, we use an ensemble method named Cascading and Sharing to derive many significant classification rules from incrementally inducted decision tree and improve the classifiers accuracy.

Keun Ho Ryu | Minghao Piao | Khalid E. K. Saeed | Jong Bum Lee

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