A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets
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),...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2013/196256 |
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| author | Yong Zhang Dapeng Wang |
| author_facet | Yong Zhang Dapeng Wang |
| author_sort | Yong Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b912f077a2bc48ec8d723fd692eeec9a |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-b912f077a2bc48ec8d723fd692eeec9a2025-02-03T01:22:54ZengWileyAbstract and Applied Analysis1085-33751687-04092013-01-01201310.1155/2013/196256196256A Cost-Sensitive Ensemble Method for Class-Imbalanced DatasetsYong Zhang0Dapeng Wang1School of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi, Dalian, Liaoning 116081, ChinaSchool of Computer and Information Technology, Liaoning Normal University, No. 1, Liushu South Street, Ganjingzi, Dalian, Liaoning 116081, ChinaIn 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.http://dx.doi.org/10.1155/2013/196256 |
| spellingShingle | Yong Zhang Dapeng Wang A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets Abstract and Applied Analysis |
| title | A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets |
| title_full | A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets |
| title_fullStr | A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets |
| title_full_unstemmed | A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets |
| title_short | A Cost-Sensitive Ensemble Method for Class-Imbalanced Datasets |
| title_sort | cost sensitive ensemble method for class imbalanced datasets |
| url | http://dx.doi.org/10.1155/2013/196256 |
| work_keys_str_mv | AT yongzhang acostsensitiveensemblemethodforclassimbalanceddatasets AT dapengwang acostsensitiveensemblemethodforclassimbalanceddatasets AT yongzhang costsensitiveensemblemethodforclassimbalanceddatasets AT dapengwang costsensitiveensemblemethodforclassimbalanceddatasets |