caa a22 4500 606159843 CHVBK 20210128100627.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00521-014-1750-6 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1750-6 Incremental multiple instance outlier detection [Elektronische Daten] [Zhigang Wang, Zengshun Zhao, Shifeng Weng, Changshui Zhang] I-MLOF algorithm is an extension of local outlier factor (LOF) algorithm in multiple instance (MI) setting. The task of I-MLOF is to identify MI outlier. However, I-MLOF algorithm works in batch mode, where all samples must be provided for once. In some real applications such as industrial detection and traffic monitoring, MI outlier is required to be identified from data stream. The batch-mode outlier detection methods usually cannot be applied directly to these applications. In this paper, an incremental MI outlier detection algorithm "Inc I-MLOF” is proposed. MI outlier detection can be done for sequentially arrived data with Inc I-MLOF. We prove theoretically that Inc I-MLOF achieves the equal result to that of I-MLOF. The experimental results illustrate Inc I-MLOF achieves good performance on several synthetic and real data sets. The Natural Computing Applications Forum, 2014 Incremental nationallicence Outlier detection nationallicence Multiple instance nationallicence LOF nationallicence Wang Zhigang State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 100084, Beijing, China aut Zhao Zengshun College of Electronics, Communication and Physics, Shandong University of Science and Technology, 266590, Qingdao, China aut Weng Shifeng School of Electronics and Information, Zhejiang Wanli University, 315100, Ningbo, China aut Zhang Changshui State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 100084, Beijing, China aut Neural Computing and Applications Springer London 26/4(2015-05-01), 957-968 0941-0643 26:4<957 2015 26 521 https://doi.org/10.1007/s00521-014-1750-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1750-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Zhigang State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 700 1- Zhao Zengshun College of Electronics, Communication and Physics, Shandong University of Science and Technology, 266590, Qingdao, China aut NATIONALLICENCE 700 1- Weng Shifeng School of Electronics and Information, Zhejiang Wanli University, 315100, Ningbo, China aut NATIONALLICENCE 700 1- Zhang Changshui State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, 100084, Beijing, China aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/4(2015-05-01), 957-968 0941-0643 26:4<957 2015 26 521