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Learning from class-imbalanced data: Review of methods and applications
Haixiang, Guo; Yijing, Li; Shang, Jennifer; Mingyun, Gu; Yuanyue, Huang; Bing, Gong
2017
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
Volume73Pages:220-239
AbstractRare events, especially those that could potentially negatively impact society, often require humans' decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from, the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields. (C) 2016 Elsevier Ltd. All rights reserved.
KeywordRare events lmbalanced data Machine learning Data mining
DOI10.1016/j.eswa.2016.12.035
Language英语
ISSN0957-4174
Indexed ByEI ; SSCI ; SCI
EI Accession Number20170303252747
WOS IDWOS:000394632200016
PublisherPERGAMON-ELSEVIER SCIENCE LTD
Publication PlaceTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
EISSN1873-6793
Citation statistics
Cited Times:1236[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.cug.edu.cn/handle/2XU834YA/276445
Collection教学院系_工程学院
Corresponding AuthorHaixiang, Guo
Affiliation1.College of Economics and Management, China University of Geosciences, Wuhan, 430074, China
2.Research Center for Digital Business Management, China University of Geosciences, Wuhan, 430074, China
3.Mineral Resource Strategy and Policy Research Center of China University of Geosciences(WUHAN), Wuhan, 43007, China
4.The Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, 15260, United States
5.Department of Industrial Engineering, Business Administration and Statistic, E.T.S Industrial Engineering, Universidad Politécnica de Madrid, C/José Gutiérrez Abascal, 2, Madrid, 20086, Spain
Recommended Citation
GB/T 7714
Haixiang, Guo,Yijing, Li,Shang, Jennifer,et al. Learning from class-imbalanced data: Review of methods and applications[J]. EXPERT SYSTEMS WITH APPLICATIONS,2017,73:220-239.
APA Haixiang, Guo,Yijing, Li,Shang, Jennifer,Mingyun, Gu,Yuanyue, Huang,&Bing, Gong.(2017).Learning from class-imbalanced data: Review of methods and applications.EXPERT SYSTEMS WITH APPLICATIONS,73,220-239.
MLA Haixiang, Guo,et al."Learning from class-imbalanced data: Review of methods and applications".EXPERT SYSTEMS WITH APPLICATIONS 73(2017):220-239.
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