Abstract and Applied Analysis

A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets

Yong Zhang and Dapeng Wang

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In imbalanced learning methods, resampling methods modify an imbalanced dataset to form a balanced dataset. Balanced data sets perform better than imbalanced datasets for many base classifiers. This paper proposes a cost-sensitive ensemble method based on cost-sensitive support vector machine (SVM), and query-by-committee (QBC) to solve imbalanced data classification. The proposed method first divides the majority-class dataset into several subdatasets according to the proportion of imbalanced samples and trains subclassifiers using AdaBoost method. Then, the proposed method generates candidate training samples by QBC active learning method and uses cost-sensitive SVM to learn the training samples. By using 5 class-imbalanced datasets, experimental results show that the proposed method has higher area under ROC curve (AUC), F-measure, and G-mean than many existing class-imbalanced learning methods.

Article information

Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 196256, 6 pages.

First available in Project Euclid: 26 February 2014

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Zhang, Yong; Wang, Dapeng. A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 196256, 6 pages. doi:10.1155/2013/196256. https://projecteuclid.org/euclid.aaa/1393449788

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