What are some case studies on class imbalance in machine learning?

What are some case studies on class imbalance in machine learning?

Class imbalance occurs when the classes in a dataset are not represented equally. One notable case study is in fraud detection, where fraudulent transactions are much rarer than legitimate ones. Researchers used techniques like SMOTE (Synthetic Minority Over-sampling Technique) to balance the dataset and improve model performance.

Can you give me another example?

Sure! Another example is in medical diagnosis, particularly in detecting rare diseases. In these cases, the number of positive cases is significantly lower than negative cases. Techniques like cost-sensitive learning and ensemble methods have been applied to address this imbalance.

What techniques are commonly used to handle class imbalance?

Common techniques include resampling methods like oversampling the minority class, undersampling the majority class, using synthetic data generation methods like SMOTE, and implementing cost-sensitive learning where different misclassification costs are assigned.

How does class imbalance affect model performance?

Class imbalance can lead to biased models that favor the majority class, resulting in poor predictive performance for the minority class. This can manifest as high accuracy but low recall for the minority class, which is often the class of interest.