Methods for solving imbalanced data
Where should we turn when confronted with the challenge of imbalanced class distribution? While a significant portion of resources in the field suggest using resampling methods, including undersampling, oversampling, and techniques such as SMOTE, it’s crucial to note that these recommendations often sidestep foundational theory and practical application.
Before diving into solutions for imbalanced classes, it’s essential first to understand their underlying nature. The issue might be better approached in specific scenarios such as anomaly detection rather than in a traditional classification problem.
In specific scenarios, the class imbalance isn’t static. It can evolve or may be influenced by the need for adequate labels. For instance, consider a system monitoring network traffic for potential security threats. Initially, threats might be rare, leading to a class imbalance. However, as the system matures and more potential...
