A SVM Classifier for Imbalanced Datasets Based on SMOTEBoost
Zhao Lin-du · Systems Engineering · 2008
Many real world data mining applications involve imbalanced data sets,where all kinds of data are unevently distributed and the particular events of interest may be very few when compared to the other classes.Data sets that contain rare events usually produces biased classifiers that have a higher predictive accuracy over the majority classes,but poorer predictive accuracy over the minority class of interest.This paper presents a novel ensemble algorithm,SMOTEBoostSVM,which balances the classes distribution with SMOTE,and combines AdaBoost algorithm with SMOTE,using SVM as weaker.Experiments on imbalanced datasets showed that the SMOTEBoostSVM algorithm performed better in classifying prediction of imblanced data sets SMOTE,AdaBoost or SVM used alone.