Improvement over Pinball Loss Support Vector Machine

TL;DR

This paper introduces a unified Pin-SVM model that simplifies optimization across all parameter values and significantly improves classification accuracy over previous Pin-SVM models, validated through extensive experiments.

Contribution

The paper proposes a unified Pin-SVM model that handles all parameter values in a single optimization framework, enhancing usability and accuracy.

Findings

Unified model valid for all $ au$ in $[-1,1]$

Significant accuracy improvements demonstrated empirically

Simplifies optimization process for Pin-SVM

Abstract

Recently, there have been several papers that discuss the extension of the Pinball loss Support Vector Machine (Pin-SVM) model, originally proposed by Huang et al.,[1][2]. Pin-SVM classifier deals with the pinball loss function, which has been defined in terms of the parameter $\tau$. The parameter $\tau$ can take values in $[ -1,1]$. The existing Pin-SVM model requires to solve the same optimization problem for all values of $\tau$ in $[ -1,1]$. In this paper, we improve the existing Pin-SVM model for the binary classification task. At first, we note that there is major difficulty in Pin-SVM model (Huang et al. [1]) for $ -1 \leq \tau < 0$. Specifically, we show that the Pin-SVM model requires the solution of different optimization problem for $ -1 \leq \tau < 0$. We further propose a unified model termed as Unified Pin-SVM which results in a QPP valid for all $-1\leq \tau \leq 1$ and hence more convenient to use. The proposed Unified Pin-SVM model can obtain a significant improvement in accuracy over the existing Pin-SVM model which has also been empirically justified by extensive numerical experiments with real-world datasets.