Statistical evaluation for quality of experience prediction based on quality of service parameters

Machine Learning (ML) provides a theoretical and methodological framework that allows to quantify the relationship between the user's Quality of Experience (OoE) and the network's Quality of Service (QoS). In the literature, several ML-based QoS/QoE correlation models have been proposed. All of those models use inductive supervised learning techniques and most of them are built in an offline batch manner using different ML methods such as: Least Squares Regression, Artificial Neural Netwcorks, Naive Bayes classifier, Support Vector Machines, k-Nearest Neighbors, Decision Trees, and Random Forest. This paper aims to evaluate these different ML methods and determine the most suitable one for the task of establishing the QoS/QoE correlation. The comparisons show that the Decision Trees and Random Forest models give the best results to this end.