Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection

Qianying Jin, Kristiaan Kerstens and Ignace van de Woestyne ()
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Qianying Jin: NUAA - Nanjing University of Aeronautics and Astronautics [Nanjing]
Ignace van de Woestyne: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven

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Abstract: Efective 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 classifcation (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 boundary enveloping the normal class. By refecting on the monotonic relation between the characteristic variables and the membership, the proposed NPFC method is in a more general form since both input-like and outputlike characteristic variables are incorporated. In addition, by allowing some of the training observations to be misclassifed, the convex- and nonconvex-NPFC methods are extended from a hard nonparametric frontier to a soft one, which also provides a more conservative boundary enclosing the normal class. Both simulation studies and a real-life data set are used to evaluate and compare the proposed NPFC methods to some well-established methods in the literature. The results show that the proposed NPFC methods have competitive classifcation performance and have consistent advantages in detecting abnormal samples, especially the nonconvex-NPFC methods.

Keywords: Nonparametric frontier; Convex; Nonconvex; Anomaly detection (search for similar items in EconPapers)
Date: 2024-03-29
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Published in OR Spectrum, 2024, 46 (4), pp.1213-1239. ⟨10.1007/s00291-024-00751-5⟩

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Related works:
Journal Article: Convex and nonconvex nonparametric frontier-based classification methods for anomaly detection (2024)
Working Paper: Convex and Nonconvex Nonparametric Frontier-based Classification Methods for Anomaly Detection (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04548588

DOI: 10.1007/s00291-024-00751-5

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