Mutual Exploration for Missing Data Imputation, QoS Parameter Selection, and QoS Prediction in 5G Networks Using a Novel Skewness Driven Distribution Imputation Algorithm, Pearson Correlation, and XGBoost
Year: 2024
Type: article
Abstract: Pre-processing is a key stage in the Machine Learning (ML) pipeline. In such a stage, data is prepared and organized for feeding it to the ML models for a prediction task. One of the problems that might incur in this stage is that data may have missing values, requiring that either the data is deleted or imputed with a data that resembles/ correlated with the original one. Imputation is desirable as having more data to feed to ML models means the model will have more context and better prediction results. From an imputation perspective, since the goal is that the imputed data faithfully relates with the original data, the result of correlation metric is desirable to show that the imputed data and original data are closely correlated. Herein, we present a novel imputation algorithm of Skewness Driven Distribution Imputation (SDDI), and evaluate its efficacy compared to multiple state- of-the-art methods including, K-Nearest Neighbors (KNN), Mean, Mode, Forward and Backward Fill (F&B-Fill) imputation methods. The comparison is done using accuracy metrics, Root Mean Square Error (RMSE), correlation and computation time. Furthermore, a correlation analysis is conducted on the subject 5G Vehicle-to-Everything (V2X) Quality of Service (QoS) dataset, aiming to enhance the understanding of parameter selection in assessing the QoS for 5G networks by providing the comparison regarding the significance in terms of correlation of various parameters in influencing 5G network’s QoS. The state-of-the-art Pearson correlation method is used for said purpose. Moreover, we exploit an Extreme Gradient Boosting or XGBoost algorithm which is an ensemble of other techniques and not as complex as deep learning algorithms, to predict QoS, given certain conditions relevant to the 5G network. The findings open new possibilities for imputing datasets, selection of parameters for QoS of 5G networks, and the application of XGBoost for QoS prediction given various input features related to the installed 5G architecture parameters, and physical scenarios affecting the QoS.
Authors: Saifullah Khan, Onel L. A. Lo'pez, Abdul Basit Khattak
Institutions: COMSATS University Islamabad, University of Oulu
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4405212347