Machine Learning Techniques for IoT Data Analytics

IoT data analytics in dynamic environments: From an automated machine learning perspective

“…ML algorithms have been widely employed in IoT data analytics applications to analyze IoT data and make decisions [28]. This Section discusses the commonly used ML algorithms for IoT data analytics in detail.…”

“…K-Nearest Neighbors (KNN) is a basic ML algorithm that can be used to solve both classification and regression problems [28]. KNNs identify the nearest k data points to each test sample in order to estimate its value or category.…”

“…KNNs identify the nearest k data points to each test sample in order to estimate its value or category. The average distances between a test sample and its neighbor samples are calculated using a distance metric, such as Euclidean or Mahalanobis distance [28]. The majority label or the average observation value of nearby samples will be assigned to each test sample [29].…”

“…NB is highly interpretable and computationally efficient. Additionally, when compared to other ML algorithms, the primary benefit of NB is that it does not need a large number of training samples [28]. However, a major limitation of NB is that it requires prior knowledge to calculate Bayesian probabilities and make predictions.…”

“…SVM is a powerful algorithm that is capable of handling nonlinear and high-dimensional data with effective regularization and generalization. It is also excessively efficient in terms of memory consumption [28]. One significant drawback of SVM is that it does not use explicit probability estimations, making it challenging to interpret the model.…”