advancing binary imbalanced classification: a novel hybrid sampling approach for noise reduction and data integrity

In machine learning, dealing with binary imbalanced data classification is challenging due to unequal class sizes, leading to model bias. we propose a unique method that uses filtering, adasyn oversampling, and enn cleaning to balance data, improve minority class accuracy, and boost overall model performance, showing significant improvements in auc, f1, and g-mean metrics.