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On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14647))

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Abstract

Feature bagging models have revealed their practical usability in various contexts, among them in outlier detection, where they build ensembles to reliably assign outlier scores to data samples. However, the interpretability of so-obtained outlier detection methods is far from achieved. Among the standard black-box models interpretability approaches, we find Shapley values that clarify the roles of single inputs. However, Shapley values are characterized by high computational runtimes that make them useful in pretty low-dimensional applications. We propose bagged Shapley values, a method to achieve interpretability of feature bagging ensembles, especially for outlier detection. The method not only assigns local importance scores to each feature of the initial space, helping to increase the interpretability but also solves the computational issue; specifically, the bagged Shapley values can be exactly computed in polynomial time.

This work was supported by the Lamarr-Institute for ML and AI, the research training group Dataninja, the Research Center Trustworthy Data Science and Security, the Federal Ministry of Education and Research of Germany and the German federal state of NRW. The Linux HPC cluster at TU Dortmund University, a project of the German Research Foundation, provided the computing power.

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Notes

  1. 1.

    https://github.com/KDD-OpenSource/ensemble_shapley.

  2. 2.

    All experiments were performed on Intel Xeon E5 CPUs. In the paper, we stick to CPUs over GPUs also when we use neural network submodels; the choice is justified by the higher amount of parallelization they allow.

  3. 3.

    The isolation forest takes about \(220\text {min}\) of CPU time. DEAN requires about \(113\text {days}\); However, the independent ensembles are easy to parallelize, and less accurate results can already be achieved with ten thousand submodels (\(27 \text {hours}\)).

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Authors and Affiliations

  1. TU Dortmund University, Dortmund, Germany

    Simon Klüttermann, Chiara Balestra & Emmanuel Müller

Authors
  1. Simon Klüttermann
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  2. Chiara Balestra
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  3. Emmanuel Müller
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Corresponding author

Correspondence to Simon Klüttermann .

Editor information

Editors and Affiliations

  1. Academia Sinica, Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Klüttermann, S., Balestra, C., Müller, E. (2024). On the Efficient Explanation of Outlier Detection Ensembles Through Shapley Values. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14647. Springer, Singapore. https://doi.org/10.1007/978-981-97-2259-4_4

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  • DOI: https://doi.org/10.1007/978-981-97-2259-4_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2261-7

  • Online ISBN: 978-981-97-2259-4

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