Economics at your fingertips  

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

Qianying Jin (), Kristiaan Kerstens and Ignace van de Woestyne ()
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
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from IESEG School of Management Contact information at EDIRC.
Bibliographic data for series maintained by Joao DA CUNHA ().

Page updated 2023-06-04
Handle: RePEc:ies:wpaper:e202302