Note(s): Referenced abbreviations: NB – Naive Bayes; DT – Decision Trees; RF – Random Forests; VS – Variable Selection; RR – Ridge Regression; GB – Gradient Boosting; GA – Genetic Algorithm; AB – Adaptive Boosting; CT – Classification Trees; LR – Logistic Regression; LD – Linear Discriminant; SA – Simulated Annealing; DA – Discriminant Analysis; MP – Multilayer Perceptron; k-NN – k-Nearest; Neighbors; IRR – Internal Rate of Return; BWM – Best-Worst Method; CBR – Case-Based Reasoning; MP – Multilayered Perceptron; MR – Multivariate Regression; SVM – Support Vector Machine; MCS – Monte Carlo Simulations; EMP – Expected Maximum Profit; SVC – Support Vector Clustering; ANN – Artificial Neural Networks; SGB – Stochastic Gradient Boosting; AHP – Analytic Hierarchy Process; IHT – Instance Hardness Threshold; PCA – Principal Component Analysis; XGBoost – Extreme Gradient Boosting; GLC – Generalised Linear Classification; CNNs – Convolutional Neural Networks; CatBoost – Categorical Gradient Boosting; KDD – Knowledge Discovery in Databases; ML-LightGBM – Light Gradient Boosting Machines; CART – Classification and Regression Trees; SMOTE – Synthetic Minority Oversampling Technique; LASSO – Least Absolute Shrinkage and Selection Operator; TOPSIS – Technique for Order of Preference by Similarity to Ideal Solution; Borderline-SMOTE – Modified Synthetic Minority Oversampling Technique

Source(s): Own elaboration