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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 4/2015

01.05.2015 | Original Article

Incremental multiple instance outlier detection

verfasst von: Zhigang Wang, Zengshun Zhao, Shifeng Weng, Changshui Zhang

Erschienen in: Neural Computing and Applications | Ausgabe 4/2015

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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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Metadaten
Titel
Incremental multiple instance outlier detection
verfasst von
Zhigang Wang
Zengshun Zhao
Shifeng Weng
Changshui Zhang
Publikationsdatum
01.05.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 4/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1750-6

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