A data preprocessing technique in machine learning used to balance an imbalanced dataset by reducing the number of instances in the majority class. This method is often applied in crypto anomaly detection where normal transactions heavily outnumber rare, abnormal events like fraud or manipulation. It aims to prevent models from developing a bias towards the over-represented class, enhancing detection accuracy.
Mechanism
Undersampling algorithms remove instances from the majority class, either randomly or based on specific criteria, such as proximity to minority class examples or regions of high density. For instance, techniques like Tomek links or NearMiss identify and remove majority class examples that are considered “safe” or redundant. This process creates a more balanced training distribution for the learning algorithm, particularly useful in RFQ crypto monitoring.
Methodology
The strategic application of undersampling requires careful consideration to avoid losing valuable information from the majority class that might be important for model generalization. It often precedes the training of machine learning models for tasks like fraud detection or market surveillance in crypto investing. Performance evaluation must use metrics sensitive to class imbalance, such as precision, recall, and the F1-score, to assess the effectiveness of the technique in improving anomaly identification in institutional options trading.
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