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Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?

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Advances in Intelligent Data Analysis XIV (IDA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9385))

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Abstract

This paper addresses the task of finding outliers within each class in the context of supervised classification problems. Class-based outliers are cases that deviate too much with respect to the cases of the same class. We introduce a novel method for outlier detection in labelled data based on Random Forests and compare it with existing methods both on artificial and real-world data. We show that it is competitive with the existing methods and sometimes gives more intuitive results. We also provide an overview for outlier detection in labelled data. The main contribution are two methods for class-based outlier description and interpretation.

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Notes

  1. 1.

    https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#outliers.

  2. 2.

    http://docs.rapidminer.com/studio/operators/data_transformation/data_cleansing/outlier_detection/detect_outlier_cof.html.

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Acknowledgments

We thanks to IDA reviewers for valuable comments and suggestions and to Vaclav Blahut for implementation and experiments with CB-ILP. We would like to thank also to the members of KDLab FI MU for their help. This work has been partially supported by Faculty of Informatics, Masaryk University, Brno.

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Authors and Affiliations

  1. KD Lab, FI MU Brno, Brno, Czech Republic

    Leona Nezvalová, Luboš Popelínský, Luis Torgo & Karel Vaculík

  2. Faculty of Science, University of Porto, Porto, Portugal

    Leona Nezvalová, Luboš Popelínský, Luis Torgo & Karel Vaculík

Authors
  1. Leona Nezvalová
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  2. Luboš Popelínský
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  3. Luis Torgo
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  4. Karel Vaculík
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Corresponding author

Correspondence to Luboš Popelínský .

Editor information

Editors and Affiliations

  1. Université de Saint-Etienne, Saint-Etienne, France

    Elisa Fromont

  2. Intelligent Systems Lab, University of Bristol Intelligent Systems Lab, Bristol, United Kingdom

    Tijl De Bie

  3. Informatics Section, Katholieke Universiteit Leuven, Leuven, Belgium

    Matthijs van Leeuwen

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Nezvalová, L., Popelínský, L., Torgo, L., Vaculík, K. (2015). Class-Based Outlier Detection: Staying Zombies or Awaiting for Resurrection?. In: Fromont, E., De Bie, T., van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV. IDA 2015. Lecture Notes in Computer Science(), vol 9385. Springer, Cham. https://doi.org/10.1007/978-3-319-24465-5_17

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  • DOI: https://doi.org/10.1007/978-3-319-24465-5_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24464-8

  • Online ISBN: 978-3-319-24465-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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