An Improved Nonparallel Support Vector Machine

Liming Liu, Maoxiang Chu, Rongfen Gong, Li Zhang

2020IEEE Transactions on Neural Networks and Learning Systems56 citationsDOI

Abstract

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.

Topics & Concepts

Support vector machineStructured support vector machineRelevance vector machineDecision boundaryComputer scienceMargin classifierRobustness (evolution)Nonlinear systemKernel methodArtificial intelligencePattern recognition (psychology)Classifier (UML)Feature vectorLinear classifierAlgorithmInversion (geology)Machine learningPaleontologyQuantum mechanicsChemistryGeneBiochemistryStructural basinBiologyPhysicsFace and Expression RecognitionAdvanced Algorithms and ApplicationsSpectroscopy and Chemometric Analyses