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 |