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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2016: PRICAI 2016: Trends in Artificial Intelligence pp 446–456Cite as

A Novel Isolation-Based Outlier Detection Method

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

Abstract

Outlier detection is one of the most important tasks in data analysis. It refers to the process of recognizing unusual characteristics which may provide useful insights in helping us to understand the behaviors of data. In the paper, an isolation-based outlier detection method, called Entropy-based Greedy Isolation Tree (EGiTree), is proposed. Unlike other tree-like detection methods, our method exploits a half-baked isolation tree, which is constructed via three entropy-based heuristics, to identify outliers. Specifically, the heuristics are used to guide the selection process of attribute and its split value when constructing the tree. Thus, the outlierness score of each data point is estimated based on the total partition cost of the isolation node in the tree, as well as the path length and complexity of partition. Experiment results on public real-world datasets show that our approach outperforms distanced-based, density-based, subspace-based as well as state-of-the-art isolation-based approaches.

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Acknowledgement

This work is partially supported by the NSF of China (No. 61272468, 61272007, 61572443), the NSF of Zhejiang Province (No. LY14F020012, No. LQ13F020026), and the Opening Fund of Top Key Discipline of Computer Software and Theory in Zhejiang Provincial Colleges at Zhejiang Normal University (No. ZSDZZZZXK27).

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Authors and Affiliations

  1. Department of Computer Science, Zhejiang Normal University, Jinhua, 321004, China

    Yanhui Shen, Huawen Liu, Yanxia Wang, Zhongyu Chen & Guanghua Sun

Authors
  1. Yanhui Shen
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  2. Huawen Liu
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  3. Yanxia Wang
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  4. Zhongyu Chen
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  5. Guanghua Sun
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Corresponding author

Correspondence to Yanxia Wang .

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Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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Shen, Y., Liu, H., Wang, Y., Chen, Z., Sun, G. (2016). A Novel Isolation-Based Outlier Detection Method. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_37

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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