Clustering Based Oversampling Approach for Minority Class Data
Zhang Hua-xian · Information Technology and Informatization · 2011
SMOTE is a popular oversampling method in dealing with the classification of imbalanced data sets,which produces instances by linear interpolation between existing minority instances.The main disadvantage is that after SMOTE,it's still intensive where it's intensive and still sparse where it's sparse in sample space.So we propose a cluster-based oversampling method(C-SMOTE)to overcome this disadvantage.In this method,the minority class samples will be clustered into several groups firstly,and then groups are used as units to generate new samples with SMOTE.Experimental results show that our method works well in improving the classification accuracy for minority class without reducing the overall accuracy.