A Novel Approach of Ensemble Learning with Feature Reduction for Classification of Binary and Multiclass IoT Data

Abstract

The number of network and sensor-enabled devices in the Internet of Things (IoT) domains is growing extremely, leading to a huge production of data. These data contain important information which can be used in various areas, such as science, industry, medical, and even social life. To make the IoT system smart, the only solution is entering the world of machine learning. Many machine learning algorithms are introduced for handling such a huge amount of IoT data. It is very difficult to find the best-suited algorithm for problems in the IoT domain. This study combined three ensemble models and proposed a new model termed the “hybrid model”. A set of features are extracted from the raw IoT datasets from diverse IoT domains, using Principal component analysis (PCA), Linear discriminant analysis (LDA), and Isomap for classification problems. Performance comparison of the classifiers is provided in terms of their accuracy, area under the curve (AUC), and F1 score. This comparative study’s experimental result  shows that Hybrid with PCA and Stacking ensemble technique in particular with PCA have better overall performance than other ensemble techniques for binary class and multie class datasets respectively