A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
Joint Authors
Source
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-04-16
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Yong& Wang, Dapeng. 2013. A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets. Abstract and Applied Analysis،Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-453676
Modern Language Association (MLA)
Zhang, Yong& Wang, Dapeng. A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets. Abstract and Applied Analysis No. 2013 (2013), pp.1-6.
https://search.emarefa.net/detail/BIM-453676
American Medical Association (AMA)
Zhang, Yong& Wang, Dapeng. A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets. Abstract and Applied Analysis. 2013. Vol. 2013, no. 2013, pp.1-6.
https://search.emarefa.net/detail/BIM-453676
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-453676