GOBoost: G-mean optimized boosting framework for class imbalance learning

Yang Lu*, Yiu Ming CHEUNG, Yuan Yan Tang

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 2016 12th World Congress on Intelligent Control and Automation, WCICA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3149-3154
Number of pages6
ISBN (Electronic)9781467384148
DOIs
Publication statusPublished - 27 Sep 2016
Event12th World Congress on Intelligent Control and Automation, WCICA 2016 - Guilin, China
Duration: 12 Jun 201615 Jun 2016

Publication series

NameProceedings of the World Congress on Intelligent Control and Automation (WCICA)
Volume2016-September

Conference

Conference12th World Congress on Intelligent Control and Automation, WCICA 2016
Country/TerritoryChina
CityGuilin
Period12/06/1615/06/16

Scopus Subject Areas

  • Control and Systems Engineering
  • Software
  • Computer Science Applications

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