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Comparing Anomaly Detection and Classification Algorithms: A Case Study in Two Domains

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Software Quality: Higher Software Quality through Zero Waste Development (SWQD 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 472))

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Abstract

Utilizing large data sets in practical scenarios usually requires identifying, annotating and classifying rare events or anomalies. Although several methods exists, there are two classes of algorithms: anomaly detection algorithms and classification algorithms. Both types of algorithms have different characteristics and in this paper, we set out to compare them on two cases. We use data from a neurointensive care unit and from microwave radio transmissions. We apply Isolation Forest and Random Forest algorithms to find events in the data that occur with a frequency of ca. 1%. The results show that classification algorithms (Random Forest) perform better and can achieve up to 100% accuracy, while the anomaly detection algorithms (Isolation Forest) can achieve only 73% at best. Based on the results, we conclude that it is better to invest in annotating data á priori and use classification algorithms, despite the lower costs of using the anomaly detection algorithms.

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Authors and Affiliations

  1. Chalmers | University of Gothenburg, Gothenburg, Sweden

    Miroslaw Staron

  2. Sahlgrenska University Hospital, Gothenburg, Sweden

    Helena Odenstedt Hergés

  3. Ericsson AB, Gothenburg, Sweden

    Helena Odenstedt Hergés, Linda Block & Martin Sjödin

Authors
  1. Miroslaw Staron
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  2. Helena Odenstedt Hergés
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  3. Linda Block
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  4. Martin Sjödin
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Corresponding author

Correspondence to Miroslaw Staron .

Editor information

Editors and Affiliations

  1. Blekinge Institute of Technology, Karlskrona, Sweden

    Daniel Mendez

  2. Austrian Center for Digital Production (CDP), SBA Research gGmbH, Vienna, Austria

    Dietmar Winkler

  3. fortiss GmbH, Munich, Germany

    Johannes Kross

  4. TU Wien, Vienna, Austria

    Stefan Biffl

  5. Software Quality Lab GmbH, Linz, Austria

    Johannes Bergsmann

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Staron, M., Hergés, H.O., Block, L., Sjödin, M. (2023). Comparing Anomaly Detection and Classification Algorithms: A Case Study in Two Domains. In: Mendez, D., Winkler, D., Kross, J., Biffl, S., Bergsmann, J. (eds) Software Quality: Higher Software Quality through Zero Waste Development. SWQD 2023. Lecture Notes in Business Information Processing, vol 472. Springer, Cham. https://doi.org/10.1007/978-3-031-31488-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-31488-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31487-2

  • Online ISBN: 978-3-031-31488-9

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