Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Understand

Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced class distributions.

• While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm.
• Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets.
• Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise.