stochastic/Probabilistic/fuzzy/dynamic modeling
Ali Sahleh; Maziar Salahi; Sadegh Eskandari
Abstract
Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane ...
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Purpose: The aim of this paper is to present an enhanced variant of Twin Parametric-Margin Support Vector Machine (TPMSVM) that improves classification performance.Methodology: By replacing a variable in the objective function, we keep the samples of one class farther from the parametric margin hyperplane of the other class.Findings: The enhanced model is convex for both linear and nonlinear cases. Also, numerical experiments on UCI datasets show that the enhanced model performs better compared to two similar models for both linear and nonlinear cases.Originality/Value: The previous studies of TPMSVM that increased the accuracy through approaches such as assigning weights to data sample, converting it into an unconstrained model and adding a new term in the objective function, did not guarantee that all samples will be far and on the negative side of the margin hyperplane. However, this study provides an approach to overcome this disadvantage of TPMSVM.