Table 1 The choice of hyperparameters of each model

Decision tree (C4.5)

confidence factor used for pruning (C); minimum number of instances of each leaf (N)

C = 0.1,0.15,0.2,0.25, 0.3; N = 2,3,4,5,6

C = 0.25; N = 2

Neural network

the size of network (number of hidden nodes, H); gradient descent (D).

H = 3, 4, 8, 10, 20, 50, 100 and D = 0.00001, 0.001, 0.1, 0.5, 0.9

H = 4; D = 0.1

Random forest

the depth of the tree(T); number of tree models(N)

T = 1, 2, 3, 5, 10; N = 100, 200, 300, 500

T = 8; N = 300

Bagging with C4.5 decision tree

the sampling ratio (P); number of sub-classifiers(N)

P = 70, 80, 90, 95, 100%; N = 100, 150, 200, 300, 500

P = 90%; N = 200

Boosting with C4.5 decision tree

the number of sub-classifiers(N)

N = 10, 30, 50, 100

N = 30