Table 4 Selected classifiers and their base parameters

Fine tree

Maximum splits: 100 split criterion: Gini’s Diversity Index

Medium tree

Maximum splits: 20 split criterion: Gini’s Diversity Index

Coarse tree

Maximum splits: 4 split criterion: Gini’s Diversity Index

Linear SVM

Kernel function: linear multi-class method: one-vs-one

Q-SVM

Kernel function: quadratic multi-class method: one-vs-one

Cubic SVM

Kernel function: cubic multi-class method: one-vs-one

Fine KNN

Number of neighbors: 1 distance metric: Euclidean distance weight: equal

Medium KNN

Number of neighbors: 10 distance metric: Euclidean distance weight: equal

W-KNN

Number of neighbors: 10 distance metric: Euclidean distance weight: squared inverse

Ensemble-BT

Ensemble method: AdaBoost learner type: decision tree maximum splits: 20 number of learners: 30

Ensemble subset KNN

Ensemble method: subspace learner type: nearest neighbor number of learners: 30

Ensemble RUSB

Ensemble method: RUSBoost learner type: decision tree number of learners: 30 maximum splits: 20