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Smooth pinball loss nonparallel support vector machine for robust classification
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DOI：10.1016/j.asoc.2020.106840.png)
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Abstract
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In this paper, we propose a robust smooth pinball loss nonparallel support vector machine (SpinNSVM) for binary classification. We first define a smooth pinball loss function, which is insensitive to the feature noise of samples, especially for the samples located nearby the boundary, and is able to capture the distribution of samples well simultaneously. Due to its differentiability, SpinNSVM model is formulated with two convex optimization problems. Furthermore, an efficient dual coordinate descent algorithm is adopted to solve the dual formulations of SpinNSVM. Numerical experiments are provided to demonstrate the effectiveness and the efficiency of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Support vector machine
Smooth pinball loss
Non-parallel SVM
Dual coordinate descent algorithm
Binary classification
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