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Table 4 Selected classifiers and their base parameters

From: A multilevel features selection framework for skin lesion classification

ClassifierBase parameters
Fine treeMaximum splits: 100 split criterion: Gini’s Diversity Index
Medium treeMaximum splits: 20 split criterion: Gini’s Diversity Index
Coarse treeMaximum splits: 4 split criterion: Gini’s Diversity Index
Linear SVMKernel function: linear multi-class method: one-vs-one
Q-SVMKernel function: quadratic multi-class method: one-vs-one
Cubic SVMKernel function: cubic multi-class method: one-vs-one
Fine KNNNumber of neighbors: 1 distance metric: Euclidean distance weight: equal
Medium KNNNumber of neighbors: 10 distance metric: Euclidean distance weight: equal
W-KNNNumber of neighbors: 10 distance metric: Euclidean distance weight: squared inverse
Ensemble-BTEnsemble method: AdaBoost learner type: decision tree maximum splits: 20 number of learners: 30
Ensemble subset KNNEnsemble method: subspace learner type: nearest neighbor number of learners: 30
Ensemble RUSBEnsemble method: RUSBoost learner type: decision tree number of learners: 30 maximum splits: 20