Abstract
Machine learning is one of key-enablers in case of novel usage scenarios and adaptive behavior within next generation mobile networks. In this paper, it is examined how model-driven approach can be adopted to automatize machine learning tasks aiming mobile network data analysis. The framework is evaluated on classification task for purpose of base station anomaly detection relying on Neo4j graph database. According to the experiments performed on publicly available dataset, such approach shows promising results when it comes to both classification performance and reducing the time required for operations related to data import and model training.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
