4.5 Article

Robust Parametric Twin Support Vector Machine for Pattern Classification

Journal

NEURAL PROCESSING LETTERS
Volume 47, Issue 1, Pages 293-323

Publisher

SPRINGER
DOI: 10.1007/s11063-017-9633-3

Keywords

Twin support vector machines; Parametric twin support vector machine; Hetroscedastic noise; Parametric insensitive model; Multi-category classification

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In this paper, we propose a robust parametric twin support vector machine (RPTWSVM) classifier based on Parametric--Support Vector Machine (Par--SVM) and twin support vector machine. In order to capture heteroscedastic noise present in the training data, RPTWSVM finds a pair of parametric margin hyperplanes that automatically adjusts the parametric insensitive margin to incorporate the structural information of data. The proposed model of RPTWSVM is not only useful in controlling the heteroscedastic noise but also has much faster training speed when compared to Par--SVM. Experimental results on several machine learning benchmark datasets show the advantages of RPTWSVM both in terms of generalization ability and training speed over other related models.

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