期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 26, 期 2, 页码 468-484出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2012.235
关键词
Data streams; data of uncertainty
类别
资金
- US National Science Foundation [IIS-0905215, CNS-1115234, IIS-0914934, DBI-0960443, OISE-1129076]
- US Department of Army [W911NF-12-1-0066]
- Google Mobile Program and KAU grant
- Natural Science Foundation of China [61070033, 61203280, 61202270]
- Guangdong Natural Science Funds for Distinguished Young Scholar [S2013050014133]
- Natural Science Foundation of Guangdong province [9251009001000005, S2011040004187, S2012040007078]
- Specialized Research Fund for the Doctoral Program of Higher Education [20124420120004]
- Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, Overseas Outstanding Doctoral Fund [405120095]
- Australian Research Council Discovery Grant [DP1096218, DP130102691]
- ARC [LP100200774, LP120100566]
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1115234] Funding Source: National Science Foundation
This paper presents a novel framework to uncertain one-class learning and concept summarization learning on uncertain data streams. Our proposed framework consists of two parts. First, we put forward uncertain one-class learning to cope with data of uncertainty. We first propose a local kernel-density-based method to generate a bound score for each instance, which refines the location of the corresponding instance, and then construct an uncertain one-class classifier (UOCC) by incorporating the generated bound score into a one-class SVM-based learning phase. Second, we propose a support vectors (SVs)-based clustering technique to summarize the concept of the user from the history chunks by representing the chunk data using support vectors of the uncertain one-class classifier developed on each chunk, and then extend k-mean clustering method to cluster history chunks into clusters so that we can summarize concept from the history chunks. Our proposed framework explicitly addresses the problem of one-class learning and concept summarization learning on uncertain one-class data streams. Extensive experiments on uncertain data streams demonstrate that our proposed uncertain one-class learning method performs better than others, and our concept summarization method can summarize the evolving interests of the user from the history chunks.
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