Addressing Class Imbalance in IoT: A Comparative Analysis of Resampling Techniques

In modern times, automated task processing and sophisticated algorithm design are important tools for using cutting-edge technologies and approaches to extract insights from data and practical solutions. The machine learning models powered by data have produced outputs that were either more or less worthy when the input datasets were balanced. An uneven distribution of classes in the input datasets has resulted in imbalanced data. Class imbalance has been a significant challenge in machine learning applications, particularly when working with substantially disparate distributions like those found in Internet of Things datasets. This study addressed the class imbalance issue in IoT data by comparing various resampling strategies. The study aimed to find efficient ways to realign class distributions and enhance the functionality of machine learning models implemented in Internet of Things systems. A predictive model built on an unbalanced data set appeared to have high accuracy, but it struggled to generalise new data from the minority class. Resampling techniques, including Over-sampling, Under-sampling, SMOTE (Synthetic Minority Over-Sampling Technique), and ADASYN (Adaptive Synthetic Sampling), were evaluated using an extensive variety of IoT datasets spanning different classes and domains. The functionality of each technique was assessed using performance metrics such as the area covered by AUC, F1-score, precision, and recall. This study advanced the understanding of class imbalance mitigation in IoT data processing by providing insights into creating more durable and trustworthy models for IoT scenarios. CCS CONCEPTS • Class Imbalance • Applied Computing • Machine Learning • Internet of Things (IoT)