# 5G/B5G Service Classification Using Supervised Learning
## 5 Conclusions  
During selecting the network slices in new generation networks (5G/B5G), the use of KPI and KQI parameters is crucial to identify and characterize each service requested by the UE. This procedure allows service providers to allocate ad hoc resources for the  
Figure 5: Scheme of one Decision Tree of the Random Forest for the second simulation (KPIs + KQIs). Figure 6: Comparison of the results obtained by the Random Forest model in both simulations. service and, implicitly, have a better and appropriate QoS. A good classification scheme can improve network and service management, SLA compliance, and in consequence, the QoE perceived by users. This paper has proposed a system for classifying services in new generation networks based on ML. The predictive model is a fundamental block of the proposed service classifier system, which is in charge of classifying 5G services. The main limitation of the project was to have a 5G real operating dataset. It achieved the best possible classification results in our system. It was necessary to create a dataset containing KPI/KQI parameters extracted from standards documents and projects to classify 5G services. The SML algorithm generated a predictive model trained and validated using the KPI and KQI parameters to classify each service. We established two situations; classify services using only parameters KPI and applying both parameters (KPI + KQI). We implemented simulations employing five different SL algorithms (Decision Tree, Random Forest, SVM, KNN, and MLPC), and we validated the results with the K-Folds technique. Analyzing the results produced by the confusion matrices and applying equations to evaluate performance indicates that it is possible to compare the proposed ML algorithms. Furthermore, comparing the simulation results obtained, two proposals showed similarities; both the classifications of services by Decision Trees and Random Forests are the best approaches. It is not easy to make a direct comparison between the two proposals if the characteristics or attributes differ. Incorporating KQIs allowed for improvements of 3% in accuracy and 3.41% in MCC for the classification of services using a Random Forest algorithm, as shown in Figure 6. The aim was to prove that including KQI besides KPI in the service classification will improve the identification of the services. This idea was gotten supported using ML techniques to solve a new generation telecommunication network. It was proven satisfactory according to the results. Future simulations will use a dataset of real operating values for the KPI and KQI parameters from 5G/B5G networks to better characterize the network's performance. The following are available online at [[https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)]([https://www.mdpi.com/article/10](https://www.mdpi.com/article/10)) 3390/app11114942/s1. Conceptualization, J.E.P.-V.; Methodology, J.E.P.-V.; Software, J.D.G.-F.; Supervision, C.E.A.-C., J.I.N.-H. and R.R.-R.; Writing--original draft, J.E.P.-V. and J.D.G.-F.; Writing--review and editing, J.E.P.-V., J.D.G.-F., C.E.A.-C., J.I.N.-H., R.R.-R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data available on request. The dataset which contains the 5G services and their KPI/KQI parameters is available on: Zenodo.org in the link [[https://zenodo.org/record/4779074#.YK8](https://zenodo.org/record/4779074#.YK8) rd5 MzbQQ]([https://zenodo.org/record/4779074#.YK8](https://zenodo.org/record/4779074#.YK8) rd5 MzbQQ), doi:10.5281/zenodo 4779074. Also the programming code of the simulations is available on: Zenodo.org in the link [[https://zenodo.org/record/4817139#.YK8q2](https://zenodo.org/record/4817139#.YK8q2) JMzbQQ]([https://zenodo.org/record/4817139#.YK8q2](https://zenodo.org/record/4817139#.YK8q2) JMzbQQ), doi:10.5281/zenodo 4817139, accessed 26 May 2021. This project is undertaken under a multi-institutional agreement.