<p>Class imbalance in datasets often degrades the performance of classification models. Although the Synthetic Minority Over-sampling Technique (SMOTE) and its variants alleviate this issue by generating synthetic samples, they frequently overlook local density and distribution characteristics. Consequently, developing methods that incorporate local spatial information to synthesize samples that better preserve the original data distribution is critical for improving model robustness in class-imbalanced scenarios. To address this gap, we propose an enhanced SMOTE algorithm (ISMOTE), which modifies the spatial constraints for synthetic sample generation. Unlike SMOTE, the proposed method first generates a base sample between two original samples. Then the Euclidean distance between the two samples is multiplied by a random number to generate a random quantity. This random quantity is added or subtracted based on the distance between the base sample and the original samples, ensuring that new samples are generated around the two original samples. By adaptively expanding the synthetic sample generation space, ISMOTE effectively alleviates distortions in local data distribution and density. This study compared the ISMOTE algorithm with seven mainstream oversampling algorithms, using three classifiers on thirteen public datasets from the KEEL, UCI, and Kaggle databases. Comparative analysis of 2D and 3D scatter plots revealed that ISMOTE yields more realistic data distributions. Experimental results demonstrated relative improvements in classifier performance, with F1-score, G-mean, and AUC increasing by 13.07%, 16.55%, and 7.94%, respectively. Furthermore, ISMOTE’s parameter adaptability enables its application to multi-class imbalanced datasets.</p>