Over-Sampling Minority Class is the simplest technique for handling class imbalance — duplicating or generating additional samples from the minority class to increase its representation in the training set, ensuring the model receives sufficient gradient signal from rare classes.

Over-Sampling Methods

• Random Duplication: Randomly duplicate existing minority samples — simplest approach.
• SMOTE: Generate synthetic samples by interpolating between nearest minority neighbors.
• ADASYN: Adaptively generate more synthetic samples in regions where the minority class is underrepresented.
• GAN-Based: Use GANs to generate realistic synthetic minority samples.

Why It Matters

• No Information Loss: Unlike under-sampling, over-sampling preserves all training data.
• Overfitting Risk: Exact duplication can cause the model to memorize minority examples — augmentation mitigates this.
• Semiconductor: Rare defect types need over-sampling — a model that ignores rare defects is operationally dangerous.

Over-Sampling is amplifying the rare signal — increasing minority class representation to ensure the model learns from every class.