title: A new robust model of one-class classification by interval-valued training data using the triangular kernel.
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.
authors.affiliation: Department of Control, Automation and System Analysis, Saint Petersburg State Forest Technical University, Russia. Electronic address: lev.utkin@gmail.com.
authors.forename: Lev V
authors.initials: LV
authors.lastname: Utkin
authors.affiliation: Department of Computer Science, Saint Petersburg State Electrotechnical University, Russia. Electronic address: anatoly.chekh@gmail.com.
authors.forename: Anatoly I
authors.initials: AI
authors.lastname: Chekh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016014:Linear Models; D008962:Models, Theoretical; D016011:Normal Distribution; D060388:Support Vector Machine
keywords: Extreme points; Interval-valued data; Kernel; Linear programming; Minimax strategy; Novelty detection; One-class classification; Support vector machine