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

Qianying Jin (qianying.jin@nuaa.edu.cn), Kristiaan Kerstens and Ignace van de Woestyne (ignace.vandewoestyne@kuleuven.be)
Additional contact information
Qianying Jin: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
Ignace van de Woestyne: KU Leuven, Research Centre for Operations Research and Statistics (ORSTAT), Brussels Campus, War- moesberg 26, B-1000 Brussels, Belgium

No 2023-EQM-01, Working Papers from IESEG School of Management

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

Keywords: : Nonparametric Frontier; Convex; Nonconvex; Anomaly Detection (search for similar items in EconPapers)
Pages: 26 pages
Date: 2023-02
New Economics Papers: this item is included in nep-eff
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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 (2024)
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