Imbalanced Data describes a condition in a dataset where the distribution of classes is skewed, meaning one class significantly outnumbers the other(s). In crypto and financial modeling, this frequently occurs in fraud detection, rare event prediction, or anomaly identification. Such imbalance presents notable challenges for standard machine learning algorithms.
Mechanism
Imbalance arises from the inherent nature of certain phenomena, such as a low incidence of fraudulent transactions compared to legitimate ones. Machine learning models trained on such data tend to bias predictions towards the majority class. This leads to poor performance on the minority class due to its underrepresentation in the training examples.
Methodology
Addressing imbalanced data involves techniques such as oversampling the minority class, using methods like SMOTE or ADASYN, or undersampling the majority class, with techniques like Tomek Links or NearMiss. Cost-sensitive learning algorithms, ensemble methods, and synthetic data generation are also employed to re-weight classes or adjust decision boundaries. These methods improve the model’s ability to learn from and accurately predict rare events.
Hybrid resampling techniques optimize block trade anomaly detection by rebalancing imbalanced data, enabling robust signal extraction for superior execution.
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