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A novel ensemble learning technique of shallow models applied on a COVID-19 dataset

Babuc D.

Annals of Computer Science and Information Systems

2024

Vol. 39

537--542

Our lives were affected by the COVID-19 pandemic. In order to face this crisis, we provided a novel ensemble learning strategy to tackle the COVID-19 prediction and classification problems. Because of their capacity to handle the complex and varied nature of COVID-19 data, a range of shallow models, including K-Nearest Neighbors, Decision Trees, Support Vector Machines, Classification and Regression Trees, and Extreme Gradient Boost, are included in our method. Using a COVID-19 dataset, each model is trained independently and then ensemble learning techniques are used to integrate the predictions of the models. We use strict model validation and hyperparameter optimization to improve performance. Comparing our ensemble method to a single model or traditional ensemble techniques, our results show considerable improvements in classification performance and prediction accuracy.