Adaptive Weighting-Synthetic Minority Oversampling Technology

Class imbalance is prevalent in real-world datasets. Minority samples are far fewer than majority samples. Traditional classifier design typically assumes balanced data, which causes classifiers to favor the majority class when faced with imbalanced datasets. Thus, there are high misclassification costs for minority classes in critical fields like healthcare and finance. Most existing oversampling methods for handling imbalance problems, such as SMOTE (The Synthetic Minority Oversampling Technique), suffer from limitations like noise sensitivity, ignorance of minority class sub-cluster structures, and poor adaptability to the heterogeneity of sample distributions. This article focuses on addressing these issues by proposing a novel oversampling algorithm, which named Adaptive Weighting-Synthetic Minority Oversampling Technology (AW-SMOTE). It consists of three progressive stages. In the first stage, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering identifies the distribution structure of the minority samples. It divides potential sub-clusters while removing noise interference. A clear data foundation for subsequent sampling is established. In the second stage, the most representative boundary sample in each cluster is used to evaluate the weight of each cluster. The total number of synthetic samples is allocated to different clusters according to the weight. It provides global sample enhancement support. In the third stage, adaptive sample generation is performed within each cluster. It combines the two perspectives of boundary tightness and local density. The Sigmoid function is used to dynamically adjust the weight ratio. Finally, new samples are synthesized in key regions that both preserve distribution characteristics and enhance discriminability in classification. Through experiments on standard datasets from KEEL repositories, the feasibility and effectiveness of this algorithm are demonstrated.