SMOTE k-out: Enhancing Class Separability through Outer Synthetic Sampling

Abstract

[Abstract]: Oversampling techniques are commonly used to address class imbalance in supervised classification, with SMOTE being a popular approach. However, traditional SMOTE generates synthetic samples within the neighbourhood of minority instances, which can increase data complexity and hinder class separability. This work proposes SMOTE k-out, which creates synthetic samples outside the local neighbourhood to increase minority class sparsity. This aims to reduce overfitting and mitigate the impact of noise, thereby improving the definition of the decision boundary. Experiments on multiple imbalanced datasets demonstrate that SMOTE k-out consistently reduces complexity and achieves higher accuracy and F-measure, particularly with SVM and LDA classifiers.