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GOBoost: G-mean optimized boosting framework for class imbalance learning
Yang Lu1; Yiu-ming Cheung1,2; Yuan Yan Tang3
2016-09-27
Conference Name2016 12th World Congress on Intelligent Control and Automation (WCICA)
Source PublicationProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume2016-September
Pages3149-3154
Conference Date12-15 June 2016
Conference PlaceGuilin, China
CountryChina
PublisherIEEE
Abstract

Boosting-based methods are effective for class imbalance problem, where the numbers of samples in two or more classes are severely unequal. However, the classifier weights of existing boosting-based methods are calculated by minimizing the error rate, which is inconsistent with the objective of class imbalance learning. As a result, the classifier weights cannot represent the performance of individual classifiers properly when the data is imbalanced. In this paper, we therefore propose a G-mean Optimized Boosting (GOBoost) framework to assign classifier weights optimized on G-mean. Subsequently, high weights are assigned to the classifier with high accuracy on both the majority class and the minority class. The GOBoost framework can be applied to any AdaBoost-based method for class imbalance learning by simply replacing the calculation of classifier weights. Accordingly, we extend six AdaBoost-based methods to GOBoost-based methods for comparative studies in class imbalance learning. The experiments conducted on 12 real class imbalance data sets show that GOBoost-based methods significantly outperform the corresponding AdaBoost-based methods in terms of F1 and G-mean metrics.

DOIhttps://doi.org/10.1109/WCICA.2016.7578792
URLView the original
Indexed BySCI
Language英语
WOS Research AreaAutomation & Control Systems ; Engineering
WOS SubjectAutomation & Control Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000388373803029
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Citation statistics
Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer Science, Hong Kong Baptist University (HKBU), Hong Kong, China
2.HKBU Institute of Research and Continuing Education, Shenzhen, China.
3.Department of Computer and Information Science, Faculty of Science and Technology, University of Macau, Macau, China
Recommended Citation
GB/T 7714
Yang Lu,Yiu-ming Cheung,Yuan Yan Tang. GOBoost: G-mean optimized boosting framework for class imbalance learning[C]:IEEE,2016:3149-3154.
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