Machine Learning-Based Boosted Regression Ensemble Combined with Hyperparameter Tuning for Optimal Adaptive Learning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 10; p. 3776
• Main Authors: Isabona, Joseph, Imoize, Agbotiname Lucky, Kim, Yongsung
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 16.05.2022; MDPI
• Subjects: 5G performance measurement; Accuracy; Adaptive learning; Algorithms; Analysis; Artificial Intelligence; Bayes Theorem; Big data; Communications industry; Data analysis; Data processing; Datasets; Decision making; hyperparameter tuning; Least-Squares Analysis; Machine Learning; Optimization; random forest; Random variables; Regression analysis; Support vector machines; Telecommunications services industry; throughput data; Germany; optimization; throughput data; 5G performance measurement; least-squares boosting; random forest; machine learning; adaptive learning; hyperparameter tuning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22103776

Over the past couple of decades, many telecommunication industries have passed through the different facets of the digital revolution by integrating artificial intelligence (AI) techniques into the way they run and define their processes. Relevant data acquisition, analysis, harnessing, and mining are now fully considered vital drivers for business growth in these industries. Machine learning, a subset of artificial intelligence (AI), can assist, particularly in learning patterns in big data chunks, intelligent extrapolative extraction of data and automatic decision-making in predictive learning. Firstly, in this paper, a detailed performance benchmarking of adaptive learning capacities of different key machine-learning-based regression models is provided for extrapolative analysis of throughput data acquired at the different user communication distances to the gNodeB transmitter in 5G new radio networks. Secondly, a random forest (RF)-based machine learning model combined with a least-squares boosting algorithm and Bayesian hyperparameter tuning method for further extrapolative analysis of the acquired throughput data is proposed. The proposed model is herein referred to as the RF-LS-BPT method. While the least-squares boosting algorithm is engaged to turn the possible RF weak learners to form stronger ones, resulting in a single strong prediction model, the Bayesian hyperparameter tuning automatically determines the best RF hyperparameter values, thereby enabling the proposed RF-LS-BPT model to obtain desired optimal prediction performance. The application of the proposed RF-LS-BPT method showed superior prediction accuracy over the ordinary random forest model and six other machine-learning-based regression models on the acquired throughput data. The coefficient of determination (Rsq) and mean absolute error (MAE) values obtained for the throughput prediction at different user locations using the proposed RF-LS-BPT method range from 0.9800 to 0.9999 and 0.42 to 4.24, respectively. The standard RF models attained 0.9644 to 0.9944 Rsq and 5.47 to 12.56 MAE values. The improved throughput prediction accuracy of the proposed RF-LS-BPT method demonstrates the significance of hyperparameter tuning/optimization in developing precise and reliable machine-learning-based regression models. The projected model would find valuable applications in throughput estimation and modeling in 5G and beyond 5G wireless communication systems.