Advancing Binary Imbalanced Classification: A Novel Hybrid SamplingApproach for Noise Reduction and Data Integrity

In machine learning, dealing with binary imbalanced data classification ischallenging due to unequal class sizes, leading to model bias. We propose a unique methodthat uses filtering, ADASYN oversampling, and ENN cleaning to balance data, improveminority class accuracy, and boost overall model performance, showing significantimprovements in AUC, F1, and G-mean metrics.

Advancing Binary Imbalanced Classification: A Novel Hybrid SamplingApproach for Noise Reduction and Data Integrity Keywords:

Imbalanced learning , Sampling technique , Classification , ADASYN