Real Time Anomaly detection-Based QoE Feature selection and Ensemble Learning for HTTP Video Services.

Using Machine Learning to the user perception prediction has largely increased in the last decade. Although it is difficult to deduce the best category model to address the predicted Quality of Experience QoE in operational networks. Also, sometimes the result obtained by ML is not relevant i.e not expected according to the present conditions. We talk here about problem of anomaly in Machine Learning context. To fill this gap, in this paper, we try to solve the problem of Machine Learning ML anomaly detection in the QoE context for video streaming service in Software Defined Networking SDN environment. We explore in one hand feature selection methodology to extract the most correlated features to the video QoE. In other hand we investigate different ensemble learning approaches to improve the detection of QoE anomalies following the known stacking or super learning model. Results show that the proposed model presents a high performance comparing to other types of ensemble learning and single learning techniques.