Borderline-SMOTE is an advanced technique used in machine learning to address the problem of class imbalance in datasets. It enhances the SMOTE (Synthetic Minority Over-sampling Technique) method by focusing on the samples that are near the decision boundary between classes. This helps generate synthetic examples that are more informative and relevant, improving the model's ability to distinguish between classes. The process involves identifying the minority class samples that are close to the majority class samples, known as borderline instances. By creating new synthetic instances around these borderline points, Borderline-SMOTE aims to strengthen the classifier's performance, particularly in challenging scenarios where classes overlap significantly.