LD-SMOTE: A Novel Local Density Estimation-Based Oversampling Method for Imbalanced Datasets
Jiacheng Lyu, Jie Yang, Zhixun Su, Zilu Zhu · Symmetry · 2025
Imbalanced data have become an essential stumbling block in the field of machine learning. In this paper, a novel oversampling method based on local density estimation, namely LD-SMOTE, is presented to address constraints of the popular rebalance technique SMOTE. LD-SMOTE initiates with k-means clustering to quantificationally measure the classification contribution of each feature. Subsequently, a novel distance metric grounded in Jaccard similarity is defined, which accentuates the features that are more intricately linked to the minority class. Utilizing this metric, we estimate the local density with a Gaussian-like function to control the quantity of synthetic samples around every minority sample, thus simulating the distribution of the minority class. Additionally, the generation of synthetic samples occurs within a triangular region constructed by this minority sample and its two chosen neighbors in LD-SMOTE, instead of on the line connecting the minority sample and one of its neighbors. Experimental comparisons between LD-SMOTE and 16 existing resampling methods on 19 datasets reveal a significant average increase in LD-SMOTE with 6.4% in accuracy, 4.4% in the F-measure, 5.4% in the G-mean, and 4.0% in AUC. This result indicates that LD-SMOTE can be an alternative oversampling method for imbalanced datasets.