A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications
文献类型：期刊论文
| 作者 | Wu, Di1,2 ; Luo, Xin1; Wang, Guoyin1; Shang, Mingsheng1; Yuan, Ye1,2; Yan, Huyong3
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| 刊名 | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
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| 出版日期 | 2018-03-01 |
| 卷号 | 14期号:3页码:909-920 |
| 关键词 | Differential evolution (DE) general framework industrial application positioning optimization self-labeled semi-supervised classification (SSC) |
| ISSN号 | 1551-3203 |
| DOI | 10.1109/TII.2017.2737827 |
| 英文摘要 | Self-labeled technique, a paradigm of semisupervised classification (SSC), is highly effective in alleviating the shortage of labeled data in classification tasks via an iterative self-labeling process. Although existing self-labeled SSC models show great prospect in industrial applications, they suffer from performance degeneration caused by false-positive label-predictions of unlabeled data during the iterative self-labeling process. For addressing this issue, this paper proposes a novel SSC framework, which is highly compatible with most existing self-labeled SSC models. The main idea of this framework is to incorporate a differential-evolution-based positioning optimization algorithm for classification into the iterative self-labeling process, aiming at optimizing the positioning of newly labeled data. Specifically, five representative self-labeled SSC models with different characteristics are modified based on the proposed framework to check their performances. Experimental results on 45 benchmark datasets demonstrate that the proposed framework is highly compatible with tested self-labeled SSC models, and significantly effective in improving their performances. |
| 资助项目 | National Key Research and Development Program of China[2017YFC0804002] ; National Natural Science Foundation of China[61702475] ; National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[61602434] ; National Natural Science Foundation of China[51609229] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences |
| WOS研究方向 | Automation & Control Systems ; Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000426700600009 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/6279]  |
| 专题 | 大数据挖掘及应用中心 |
| 通讯作者 | Wang, Guoyin; Shang, Mingsheng |
| 作者单位 | 1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chongqing Technol & Business Univ, Sch Comp Sci & Informat Engn, Chongqing 400067, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Di,Luo, Xin,Wang, Guoyin,et al. A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications[J]. IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,2018,14(3):909-920. |
| APA | Wu, Di,Luo, Xin,Wang, Guoyin,Shang, Mingsheng,Yuan, Ye,&Yan, Huyong.(2018).A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications.IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS,14(3),909-920. |
| MLA | Wu, Di,et al."A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications".IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 14.3(2018):909-920. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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