Oversampling Algorithm Based on Improved K-Means and Gaussian Distribution

Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certain extent. However, the quality of clustering results has a significant impact on the final classification performance. To address this problem, an oversampling algorithm based on the Gaussian distribution oversampling algorithm and the K-means clustering algorithm combining compactness and separateness (CSKGO) is proposed in this paper. The algorithm first uses the K-means clustering algorithm, combining compactness and separateness to cluster the minority samples, constructs the cluster compactness index and inter-cluster separateness index to obtain the optimal number of clusters and the clustering results, and obtains the local distribution characteristics of the minority samples through clustering. Secondly, the sampling ratio for each cluster is assigned based on the compactness of the clustering results to determine the number of samples for each cluster in the minority class. Then, the mean vectors and covariance matrices of each cluster are calculated, and the Gaussian distribution oversampling algorithm is used to generate new samples that match the distribution of characteristics of the real minority samples, which are combined with the majority samples to form balanced data. To verify the effectiveness of the proposed algorithm, 24 datasets were selected from the University of California Irvine (UCI) Repository, and they were oversampled using the CSKGO algorithm proposed in this paper and other oversampling algorithms, respectively. Finally, these datasets were classified using Random Forest, Support Vector Machine, and K-Nearest Neighbor Classifiers. The results indicate that the algorithm proposed in this paper has higher accuracy, F-measure, G-mean, and AUC values, which can effectively improve the classification performance of the imbalanced datasets.