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Convex and Nonconvex Nonparametric Frontier-based Classification Methods for Anomaly Detection

Author

Listed:
  • Qianying JIN

    (College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China)

  • Kristiaan KERSTENS

    (Univ. Lille, CNRS, IESEG School of Management, UMR 9221 - LEM - Lille E´conomie Management, Lille, France)

  • Ignace VAN DE WOESTYNE

    (KU Leuven, Research Centre for Operations Research and Statistics (ORSTAT), Brussels Campus, War- moesberg 26, B-1000 Brussels, Belgium)

Abstract

Effective methods for determining the boundary of the normal class are very useful for detecting anomalies in commercial or security applications - a problem known as anomaly detection. This contribution proposes a nonparametric frontier-based clas- sification (NPFC) method for anomaly detection. By relaxing the commonly used convexity assumption in the literature, a nonconvex NPFC method is constructed and the nonconvex nonparametric frontier turns out to provide a more conservative bound- ary enveloping the normal class. By reflecting on the monotonic relation between the characteristic variables and the membership, the proposed NPFC method is in a more general form since both input-type and output-type characteristic variables are incor- porated. A biomedical data set is used to test the performance of the proposed NPFC methods. The results show that the proposed NPFC methods have competitive clas- sification performance and have consistent advantages in detecting abnormal samples, especially the nonconvex NPFC method

Suggested Citation

  • Qianying JIN & Kristiaan KERSTENS & Ignace VAN DE WOESTYNE, 2023. "Convex and Nonconvex Nonparametric Frontier-based Classification Methods for Anomaly Detection," Working Papers 2023-EQM-01, IESEG School of Management.
    • Handle: RePEc:ies:wpaper:e202302
    as

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    References listed on IDEAS

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    Keywords

    : Nonparametric Frontier; Convex; Nonconvex; Anomaly Detection;
    All these keywords.

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