UM
Using Graph-Based Ensemble Learning to Classify Imbalanced Data
Qin, Anyong; Shang, Zhaowei; Tian, Jinyu; Zhang, Taiping; Wang, Yulong; Tang, Yuan Yan; IEEE
2017
Conference Name2017 3RD IEEE INTERNATIONAL CONFERENCE ON CYBERNETICS (CYBCONF)
Pages265-270
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractThe class imbalance problems have attracted considerable attention from researchers of different fields. Ensemble learning has emerged as a powerful approach to address the imbalanced data and improved accuracy and robustness over the single model. In this paper, we present a novel ensemble method based on a bipartite graph (GraphEL) by maximizing the consensus among the multiple binary models. In this bipartite graph, we take into account the probability offered by the multiple classifiers and the average distance provided by the original data, which appear in the graph in the form of weights. Experimental results on 22 imbalanced data sets demonstrate the benefits of the proposed method over the conventional imbalance data handing methods.
KeywordImbalanced data Ensemble learning Consensus maximization
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics
WOS IDWOS:000414302500043
The Source to ArticleWOS
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Qin, Anyong,Shang, Zhaowei,Tian, Jinyu,et al. Using Graph-Based Ensemble Learning to Classify Imbalanced Data[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:265-270.
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