A Novel Online QoE Prediction Model Based on Multiclass Incremental Support Vector Machine
Yosr Ben Youssef, Mariem Afif, Riadh Ksantini, Sami Tabbane · 2018
Satisfying the user it's a primary goal to reach by telecom operators. Therefore, Quality of Experience (QoE), which is the measure of the user-perceived quality of a received service, has become a pivotal topic in the academic research. Generally, an efficient QoE model should be able to handle dynamic environments with large scale data, in order to continuously acquire feedback from the user, and then provide a real-time and accurate description of his perception. This paper proposes a novel online QoE estimation model, which is able to classify user perception toward video streaming service, using incremental multiclass SVM (multiclass-iSVM). The proposed online QoE model investigates the effectiveness of incremental learning, in order to handle large scale dynamic data and to improve prediction accuracy of QoE. In fact, it uses the mathematical properties of SVM and updates its unknown weights, as well as, the classification results incrementally, as new observations are considered. Comparative evaluation of the proposed multiclass iSVM-based QoE model is performed to show its superiority over relevant batch learning based models, in terms of QoE prediction accuracy and computational complexity. In particular, this model has achieved the highest classification rate of 89%, starting with only 10% of the dataset at the beginning of the incremental process.