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Post-boosting of classification boundary for imbalanced data using geometric mean
Du, Jie1; Vong, Chi-Man1; Pun, Chi-Man1; Wong, Pak-Kin2; Ip, Weng-Fai3
2017-09-04
Source PublicationNEURAL NETWORKS
ISSN0893-6080
Volume96Pages:101-114
Abstract

In this paper, a novel imbalance learning method for binary classes is proposed, named as Post-Boosting of classification boundary for Imbalanced data (PBI), which can significantly improve the performance of any trained neural networks (NN) classification boundary. The procedure of PBI simply consists of two steps: an (imbalanced) NN learning method is first applied to produce a classification boundary, which is then adjusted by PBI under the geometric mean (G-mean). For data imbalance, the geometric mean of the accuracies of both minority and majority classes is considered, that is statistically more suitable than the common metric accuracy. PBI also has the following advantages over traditional imbalance methods: (i) PBI can significantly improve the classification accuracy on minority class while improving or keeping that on majority class as well; (ii) PBI is suitable for large data even with high imbalance ratio (up to 0.001). For evaluation of (i), a new metric called Majority loss/Minority advance ratio (MMR) is proposed that evaluates the loss ratio of majority class to minority class. Experiments have been conducted for PBI and several imbalance learning methods over benchmark datasets of different sizes, different imbalance ratios, and different dimensionalities. By analyzing the experimental results, PBI is shown to outperform other imbalance learning methods on almost all datasets. (C) 2017 Elsevier Ltd. All rights reserved.

KeywordImbalance Learning Boosting Weighted Elm Smote
DOI10.1016/j.neunet.2017.09.004
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Neurosciences
WOS IDWOS:000413319800010
PublisherPERGAMON-ELSEVIER SCIENCE LTD
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
DEPARTMENT OF PHYSICS AND CHEMISTRY
Corresponding AuthorVong, Chi-Man
Affiliation1.Department of Computer and Information Science, University of Macau, Macau
2.Department of Electromechanical Engineering, University of Macau, Macau
3.Faculty of Science and Technology, University of Macau, Macau
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Du, Jie,Vong, Chi-Man,Pun, Chi-Man,et al. Post-boosting of classification boundary for imbalanced data using geometric mean[J]. NEURAL NETWORKS,2017,96:101-114.
APA Du, Jie,Vong, Chi-Man,Pun, Chi-Man,Wong, Pak-Kin,&Ip, Weng-Fai.(2017).Post-boosting of classification boundary for imbalanced data using geometric mean.NEURAL NETWORKS,96,101-114.
MLA Du, Jie,et al."Post-boosting of classification boundary for imbalanced data using geometric mean".NEURAL NETWORKS 96(2017):101-114.
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