A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Algorithm 4

BEBS algorithm.

(1) Input the whole dataset, the number of SVMs-, the oversampling ratio for

Extrapolation Borderline-SMOTE .

(2) Train on the original data set to fit soft margin SVM by choosing a proper

kernel and hyper-parameter in cross-validation and identify the support vectors

belonging to the minority

(3) For from 1 to :

(4)       Bootstrap on the to obtain the sampling result and

    not sampled in the turn

(5)       Get the union set as and operate extrapolation

    borderline-SMOTE with the sampling ratio on it.

(6)        and synthetic samples are united as training data set to obtain

    soft margin SVM with hyper-parameter chosen by validating the

    performance on .

(7) Output the ensemble of SVMs