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제목 Norm ball classifer for one&class classifcation
주저자 김세화
교신저자 정영선
공동저자 이경식
DOI doi.org/10.1007/s10479-021-03964-x
학술기관 Annals of Operations Research volume
발행기관 SPRINGER
논문 요약 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.
게재일자 2021년 02월 18일