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An Improved Nonparallel Support Vector Machine
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DOI：10.1109/TNNLS.2020.3027062.png)
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Abstract
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In this article, an improved nonparallel support vector machine (INPSVM) is proposed for pattern classification. INPSVM inherits almost all advantages of nonparallel support vector machine (NPSVM), i.e., the kernel trick can be directly applied for the nonlinear case and the matrix inversion is avoided. These are completely different from the twin support vector machine (TSVM). Moreover, the INPSVM classifier has some incomparable advantages over TSVM and NPSVM. First, it can effectively eliminate the negative effect of noise, especially feature noise around the decision boundary. Second, the novel classifier has higher classification accuracy for both linear and nonlinear data sets compared with the other algorithms. Finally, a large number of experiments show that INPSVM is superior to other algorithms in efficiency, accuracy, and robustness.
Keywords:
Support vector machines
Training
Machine learning algorithms
Fasteners
Learning systems
Kernel
Machine learning
Generalization performance
noise insensitivity
nonparallel support vector machine (NPSVM)
pattern classification
twin support vector machine (TSVM)
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