“…Once the structural features have been obtained, decision-making can be implemented by different machine learning algorithms. In this regard, one can exploit the main learning algorithms, in terms of supervised learning and unsupervised learning [9], and leverage some advanced algorithms, which include but are not limited to deep learning [10], transfer learning [11], kernel learning [12], ensemble learning [13], empirical learning [14], metalearning [15], multi-task learning [16], etc. Totally, these learning algorithms intend to develop computational models by using training data and then use the trained models for some tasks, such as classification, prediction, regression, clustering, and anomaly detection via test data.…”