Norm ball classifier for one-class classification

Sehwa Kim, Kyungsik Lee () and Young-Seon Jeong ()
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Sehwa Kim: Analytics and Optimization Lab., LG Display
Kyungsik Lee: Seoul National University
Young-Seon Jeong: Chonnam National University

Annals of Operations Research, 2021, vol. 303, issue 1, No 19, 433-482

Abstract: Abstract One-Class Classification (OCC) is a supervised learning technique for classification whereby the classifier is obtained only by training the objects from the target class and identifying whether new observations belong to the class or not. In this paper, we propose a novel approach to OCC, which is based on optimal covering of the target objects by ‘good’ norm balls. The proposed classifier consists of the selected norm balls from an integer programming model where the finite norm ball candidates from the target objects are used. Computational experiments were carried out to examine the performance and characteristics of the proposed classifier using artificial and real data from the UCI Repository. The results showed that the proposed model was comparable to existing OCC methods in the comparison group. In addition, the proposed model demonstrated high sparsity leading to low testing burden and robustness to noises.

Keywords: Norm ball; One-class classification; Integer programming; Set covering problem (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10479-021-03964-x

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