A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications

doi:10.1109/TII.2017.2737827

CORC > 重庆绿色智能技术研究院 > 中国科学院重庆绿色智能技术研究院 > 大数据挖掘及应用中心

	A Highly Accurate Framework for Self-Labeled Semisupervised Classification in Industrial Applications
	Wu, Di1,2 ; Luo, Xin1 ; Wang, Guoyin 1; Shang, Mingsheng1 ; Yuan, Ye1,2 ; Yan, Huyong 3
刊名	IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
	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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000426700600009
内容类型	期刊论文
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.