Abstract
We introduce a novel twin support vector machine with the generalized pinball loss function (GPin-TSVM) for solving data classification problems that are less sensitive to noise and preserve the sparsity of the solution. In addition, we use a symmetric kernel trick to enlarge GPin-TSVM to nonlinear classification problems. The developed approach is tested on numerous UCI benchmark datasets, as well as synthetic datasets in the experiments. The comparisons demonstrate that our proposed algorithm outperforms existing classifiers in terms of accuracy. Furthermore, this employed approach in handwritten digit recognition applications is examined, and the automatic feature extractor employs a convolution neural network.
Highlights
Support vector machines (SVMs) have evolved as a potent paradigm for pattern classification and regression during the last decade [1,2,3,4,5]
We conduct wide experiments on synthetic datasets and the UCI machine learning repository, and handwritten digit recognition applications are compared to standard twin SVM (TSVM), Pin-TSVM, and IPin-TSVM
The GPin-TSVM is less sensitive to noise and achieves sparsity, which is a major benefit of our proposed method
Summary
Support vector machines (SVMs) have evolved as a potent paradigm for pattern classification and regression during the last decade [1,2,3,4,5]. As a result of these advances, Rastogi [40] recently proposed the modified ( 1, 2)-insensitive zone SVM, which is called the generalized pinball loss SVM. This generalized pinball loss for the SVM model incorporates previous loss functions that provide noise sensitivity, sparsity, and approximate stability. Compared with TSVM, the loss of the generalized pinball SVM is required to solve a single large QPP, resulting in a higher computational complexity and inability to solve large scale problems. We examine the applicability of the main techniques of GPin-TSVM toward handwritten digit recognition problems compared with the standard TSVM, Pin-TSVM, and -insensitive zone TSVM (IPin-TSVM).
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