| Learning from class-imbalanced data: Review of methods and applications | |
| Haixiang, Guo; Yijing, Li; Shang, Jennifer; Mingyun, Gu; Yuanyue, Huang; Bing, Gong | |
| 2017 | |
| 发表期刊 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 卷号 | 73页码:220-239 |
| 摘要 | Rare 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. |
| 关键词 | Rare events lmbalanced data Machine learning Data mining |
| DOI | 10.1016/j.eswa.2016.12.035 |
| 语种 | 英语 |
| ISSN | 0957-4174 |
| 收录类别 | EI ; SSCI ; SCI |
| EI入藏号 | 20170303252747 |
| WOS记录号 | WOS:000394632200016 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 出版地 | THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND |
| EISSN | 1873-6793 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.cug.edu.cn/handle/2XU834YA/276445 |
| 专题 | 教学院系_工程学院 |
| 通讯作者 | Haixiang, Guo |
| 作者单位 | 1.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 |
| 推荐引用方式 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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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Learning from class （1977KB） | 期刊论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 | ||
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