Table 2.
Range and tuned values of hyperparameters of all the machine learning methods utilized in this study.
| Method | Parameters and the range | Optimum values and structure |
|---|---|---|
| DT |
• Max depth (ranged 1 to 50) • Min samples split (ranged 2 to 20) • Min samples leaf (range 1 to 20) |
• Max depth: 8 • Min samples split: 2 • Min samples leaf: 1 |
| AB |
• Number of estimators (ranged 1 to 50) • Learning rate (0.01 to 1.0) |
• Number of estimators: 5 • Learning rate: 10 |
| RF |
• Number of estimators (ranged 1 to 20) • Max depth (ranged 1 to 20) • Min sample split (ranged 2 to 20) |
• Number of estimators: 19 • Max depth: 20 • Min sample split: 2 |
| KNN | • Number of neighbors (ranged 1 to 50) | • Number of neighbors: 2 |
| EL | • Constructed based upon DT, AB, RF an KNN algorithms | • Tuned values of each DT, AB, RF an KNN methods |
| SVM |
• C hyperparameter (ranged 1 to 1000) • Kernel function (linear, polynomial, RBF, Sigmoid) • Gamma (range 1e-4 to 1.0) |
• C hyperparameter: 701 • Kernel function: RBF • Gamma: 0.33 |
| CNN |
• Number of filters (ranged 32 to 512) • Filter size (3*3, 5*5 and 7*7) • Pooling size (2*2 or 3*3) |
• Number of filters: 32 • Filter size: 5*5 • Pooling size: 2*2 |
| MLP-ANN |
• Number of hidden layers (ranged 2 to 20) • Number of neurons in each hidden layer (ranged 5 to 40) • Activation function (relu, tanh, sigmoid) • Learning rate (ranged 0.001 to 0.1) |
• Number of hidden layers: 6 • Number of neurons in each hidden layer: 33 • Activation function: relu • Learning rate: 0.001 |