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Universum parametric-margin ν-support vector machine for classification using the difference of convex functions algorithm
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DOI：10.1007/s10489-021-02402-6.png)
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Abstract
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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 nu-support vector machine with universum data (UPar-nu-SVM). Unlabeled samples can be integrated into supervised learning by means of UPar-nu-SVM. We propose a fast method to solve the suggested problem of UPar-nu-SVM. The primal problem of UPar-nu-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-nu-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.
Keywords:
Universum
Par-nu-support vector machine
Nonconvex optimization
DC programming
DCA
BDCA
Modified Newton method
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