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.

An Over Sampling Method of Unbalanced Data Based on Ant Colony Clustering

“…WOHC [10] in consideration of overlapping samples and overfitting problems in oversampling, the method of weighted oversampling is adopted to avoid overlapping samples, this method can only prevent but not eliminate overlapping samples. Literature [11] proposes an over-sampling algorithm AGNES-SMOTES based on hierarchical clustering and an improved SMOTE algorithm. The algorithm follows the following steps: filter the noise samples in the data set; Agnes algorithm was used to cluster the majority of samples and the minority of samples.…”