Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm
Abstrakt
Universum data that do not belong to any class of a classification problem can be exploited to utilize prior knowledge to improve generalization performance. In this paper, we design a novel parametric ν-support vector machine with universum data (UPar-ν-SVM).
Unlabeled samples can be integrated into supervised learning by means of UPar-ν-SVM. We propose a fast method to solve the suggested problem of UPar-ν-SVM.
The primal problem of UPar-ν-SVM, which is a nonconvex optimization problem, is transformed into an unconstrained optimization problem so that the objective function can be treated as a difference of two convex functions (DC). To solve this unconstrained problem, a boosted difference of convex functions algorithm (BDCA) based on a generalized Newton method is suggested (named DC-UPar-ν-SVM).
We examined our approach on UCI benchmark data sets, NDC data sets, a handwritten digit recognition data set, and a landmine detection data set. The experimental results confirmed the effectiveness and superiority of the proposed method for solving classification problems in comparison with other methods. (C) 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.