Research Article

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