A novel QoE model based on boosting support vector regression

Abstract

The main telecom operator goal is to build end user loyalty towards offered services. Computing the perceived quality, known, Quality of Experience (QoE) has become a crucial topic for investigation. Machine learning algorithms provide a solution to tease out the complex relationships between several influencing factors and QoE. This paper proposes a novel QoE estimation model for video service, namely, Boosting Support Vector Regression (BSVR) based QoE model. The purpose of this model is to investigate the effectiveness of combining multiple learners instead of classical individual learner, in order to improve prediction accuracy of the QoE. The BSVR is based on a combination of two principal techniques: Boosting algorithm and Support Vector Regression (SVR). More precisely, multiple SVR models were trained in an iterative boosting algorithm to create a powerful predictive model. In fact, the use of SVRs as weak learners has several advantages. First, the SVR is based on a convex optimization problem, where a global optimal solution exploits a limited number of support vectors, which results in improved prediction accuracy, while maintaining low computational complexity. Second, each SVR uses flexible Radial Basis Function (RBF) kernel function to model QoE data efficiently. Comparative evaluation of our proposed BSVR-based QoE model is performed to show its superiority over relevant ensemble learning methods and regression models based on single learner, in terms of prediction accuracy and computational complexity.