SMOTE Integrated

SMOTE (Synthetic Minority Over-sampling Technique) integrated approaches address class imbalance in datasets, a common problem hindering the performance of machine learning models. Current research focuses on combining SMOTE with various algorithms, including XGBoost, LightGBM, and neural networks, across diverse applications such as fraud detection, credit risk assessment, and healthcare monitoring. These integrated methods aim to improve model accuracy and reliability by generating synthetic minority class samples, thereby mitigating bias towards the majority class. The impact is significant, leading to more robust and effective models in various fields where imbalanced data is prevalent.