What are the differences between SMOTE and ADASYN?

What are the differences between SMOTE and ADASYN?

SMOTE (Synthetic Minority Over-sampling Technique) generates synthetic samples for the minority class, while ADASYN (Adaptive Synthetic Sampling) focuses on generating samples in regions where the minority class is underrepresented.

Can you explain how SMOTE works in more detail?

Sure! SMOTE works by selecting a minority class instance and creating synthetic examples along the line segments connecting it to its nearest neighbors.

What are the advantages of using ADASYN over SMOTE?

ADASYN adapts the number of synthetic samples based on the density of the minority class, which can lead to better performance in imbalanced datasets.

Are there any drawbacks to using these techniques?

Yes, both techniques can introduce noise if not carefully applied, and they may lead to overfitting if the synthetic samples are too similar to existing ones.

Which method would you recommend for a highly imbalanced dataset?

It depends on the specific dataset and problem, but ADASYN might be more effective for highly imbalanced datasets due to its adaptive nature.