SMOTE-ENN is a hybrid data resampling technique in machine learning, designed to address imbalanced datasets by combining synthetic oversampling with data cleaning. It utilizes the Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) to improve model performance. This approach is critical for scenarios like fraud detection in crypto.
Mechanism
First, SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances and their nearest neighbors, thereby increasing the representation of the under-sampled class. Following this, the ENN algorithm removes samples from both the minority and majority classes that are misclassified by their three nearest neighbors. This effectively cleans the decision boundaries and reduces noise.
Methodology
The approach systematically enhances the learnability of the minority class while simultaneously removing ambiguous or noisy examples that might hinder classifier performance. This two-stage process improves the overall quality of the dataset for training machine learning models. This leads to more robust and accurate predictions, particularly in scenarios where the minority class represents critical events such as security breaches or system failures.
Hybrid resampling techniques optimize block trade anomaly detection by rebalancing imbalanced data, enabling robust signal extraction for superior execution.
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