“…In this context, different types of classi ers were trained in the literature including Support Vector Machines (SVM) [6,8,11,12], Linear Discriminant Analysis (LDA) [13], Arti cial Neural Networks (ANN) [6] and Multi-Layer Perceptron (MLP) [8,9], k-Nearest Neighbors (k-NN) [8], Decision Trees (DT) [6,7] and Random Forests (RF) [5,8,9]. Other studies apply consensus models, by training and combining multiple classi ers [8,9]. While consensus models mitigate the over tting problem of single classi ers, they naturally require high computational power, especially when dealing with high dimensional data.…”