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Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning
Vong, Chi-Man1; Du, Jie1; Wong, Chi-Man1; Cao, Jiu-Wen2
2018-12
Source PublicationIEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
Volume29Issue:12Pages:6163-6177
AbstractIn this paper, a novel learning method called post-boosting using extended G-mean (PBG) is proposed for online sequential multiclass imbalance learning (OS-MIL) in neural networks. PBG is effective due to three reasons. 1) Through postadjusting a classification boundary under extended G-mean, the challenging issue of imbalanced class distribution for sequentially arriving multiclass data can be effectively resolved. 2) A newly derived update rule for online sequential learning is proposed, which produces a high G-mean for current model and simultaneously possesses almost the same information of its previous models. 3) A dynamic adjustment mechanism provided by extended G-mean is valid to deal with the unresolved challenging dense-majority problem and two dynamic changing issues, namely, dynamic changing data scarcity (DCDS) and dynamic changing data diversity (DCDD). Compared to other OS-MIL methods, PBG is highly effective on resolving DCDS, while PBG is the only method to resolve dense-majority and DCDD. Furthermore, PBG can directly and effectively handle unscaled data stream. Experiments have been conducted for PBG and two popular OS-MIL methods for neural networks under massive binary and multiclass data sets. Through the analyses of experimental results, PBG is shown to outperform the other compared methods on all data sets in various aspects including the issues of data scarcity, dense-majority, DCDS, DCDD, and unscaled data.
KeywordDynamic changing distribution extreme learning machine imbalance class distribution multiclass imbalance learning online sequential learning
DOI10.1109/TNNLS.2018.2826553
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000451230100032
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Citation statistics
Cited Times [WOS]:2   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Univ Macau, Dept Comp & Informat Sci, Macau 999078, Peoples R China;
2.Hangzhou Dianzi Univ, Dept Inst Informat & Control, Hangzhou 310000, Zhejiang, Peoples R China
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Vong, Chi-Man,Du, Jie,Wong, Chi-Man,et al. Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(12):6163-6177.
APA Vong, Chi-Man,Du, Jie,Wong, Chi-Man,&Cao, Jiu-Wen.(2018).Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(12),6163-6177.
MLA Vong, Chi-Man,et al."Postboosting Using Extended G-Mean for Online Sequential Multiclass Imbalance Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.12(2018):6163-6177.
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