A new robust model of one-class classification by interval-valued training data using the triangular kernel. (September 2015)

• Record Type: Journal Article
• Authors: Utkin, Lev V.; Chekh, Anatoly I.
• Is Part Of: Neural networks. Volume 69(2015:Sep.)
• Page Start: 99
• Page End: 110
• Publication Date: 2015-09
• Subjects: One-class classification -- Novelty detection -- Support vector machine -- Kernel -- Interval-valued data -- Minimax strategy -- Linear programming -- Extreme points; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2015.05.004
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: A robust one-class classification model as an extension of Campbell and Bennett's (C–B) novelty detection model on the case of interval-valued training data is proposed in the paper. It is shown that the dual optimization problem to a linear program in the C–B model has a nice property allowing to represent it as a set of simple linear programs. It is proposed also to replace the Gaussian kernel in the obtained linear support vector machines by the well-known triangular kernel which can be regarded as an approximation of the Gaussian kernel. This replacement allows us to get a finite set of simple linear optimization problems for dealing with interval-valued data. Numerical experiments with synthetic and real data illustrate performance of the proposed model.