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Incremental multiple instance outlier detection

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Abstract

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.

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Acknowledgments

This work is supported by 973 Program (2013CB329503), NSFC (Grant No. 91120301), NSFC (Grant No. 61403281), Open Research Project under Grant 20120105 from SKLMCCS and Beijing Municipal Education Commission Science and Technology Development Plan key project under grant KZ201210005007.

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Authors and Affiliations

  1. State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing, 100084, China

    Zhigang Wang & Changshui Zhang

  2. College of Electronics, Communication and Physics, Shandong University of Science and Technology, Qingdao, 266590, China

    Zengshun Zhao

  3. School of Electronics and Information, Zhejiang Wanli University, Ningbo, 315100, China

    Shifeng Weng

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  1. Zhigang Wang
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  2. Zengshun Zhao
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  3. Shifeng Weng
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  4. Changshui Zhang
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Correspondence to Zhigang Wang.

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Wang, Z., Zhao, Z., Weng, S. et al. Incremental multiple instance outlier detection. Neural Comput & Applic 26, 957–968 (2015). https://doi.org/10.1007/s00521-014-1750-6

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  • DOI: https://doi.org/10.1007/s00521-014-1750-6

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