Classification of Services through Feature Selection and Machine Learning in 5G Networks

Abstract

Network slicing (Ns) is a key enabling technology to support the concurrent provisioning of better quality of service (QoS) in 5G networks. These services have become essential for a telecom service provider (SP) to offer better QoS and QoE (quality of experience). The QoS parameters are used to estimate the performance of the network, and QoE determines user satisfaction with the network services. The main challenges faced by the service provider are to select the appropriate slice for each service and accurately classify these services on a timely basis to satisfy the Service level agreement (SLA) while improving the QoS and QoE. To overcome this issue, machine learning (ML) is a good solution. In this paper, we have proposed a 5G-KPQI (5G-key performance and quality indicator) model that considers the 5G service-based dataset for the 5G services classification. Next, we used feature selection (FS) methods to rank and select the best feature subset, which increases the performance of ML models and also reduces the training time required by the models. We subsequently considered various ML models to classify the services. Results demonstrate that the 5G-KPQI model ranks the features using Relief-F and mrMR methods and also reduces the training time of the model, hence improving classification performance measured by precision, accuracy, F1-score, recall, MCC, and time. The evaluation of the key approach outperforms in high classification accuracy and less training time using decision tree (DT) and random forest (RF).