AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE

Abstract

Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm - AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE - is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority subclusters on the basis of the obtained majority subclusters; select "seed sample"based on the sampling weight and probability distribution of minority subcluster; and restrict the generation of new samples in a certain area by centroid method in the sampling process. The combination of AGNES-SMOTE and SVM (Support Vector Machine) is presented to deal with imbalanced datasets classification. Experiments on UCI datasets are conducted to compare the performance of different algorithms mentioned in the literature. Experimental results indicate AGNES-SMOTE excels in synthesizing new samples and improves SVM classification performance on imbalanced datasets.