科研之友
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摘要
To improve the predictive power of classifiers against imbalanced data sets, this paper presents an ensemble-based learning algorithm as a new ensemble classifier model called as an SVM-C5.0 ensemble classifier model, SCECM. The SCECM adopts a differentiated sampling rate algorithm based on an improved Adaboost algorithm and further employs some unique classifier-selection strategy, novel classifier integration approach and original classification decision-making method. Comparative experimental results show that the proposed approach improves performance for the minority class while preserving the ability to recognize examples from the majority classes.
