ADASYN-Tomek is a hybrid data resampling technique used in machine learning to address imbalanced datasets, where one class substantially outnumbers another. It combines the Adaptive Synthetic Sampling Approach (ADASYN) with the Tomek Links method. The objective is to enhance the performance of classification models by refining the distribution and quality of the dataset.
Mechanism
The ADASYN component generates synthetic samples for the minority class, prioritizing instances that are harder to learn by assigning them higher weights. This expansion focuses on minority examples near the decision boundary. Subsequently, the Tomek Links method identifies and removes noisy or ambiguous data points by eliminating pairs of nearest neighbors from different classes, especially when one is a minority instance.
Methodology
This approach systematically expands the minority class representation and cleans the dataset’s borders to establish clearer decision boundaries for classifiers. By first synthetically increasing the count of under-sampled data points and then removing problematic examples that blur class distinctions, ADASYN-Tomek improves the discriminative power of learning algorithms in imbalanced data scenarios, which are common in crypto fraud detection.
Hybrid resampling techniques optimize block trade anomaly detection by rebalancing imbalanced data, enabling robust signal extraction for superior execution.
We use cookies to personalize content and marketing, and to analyze our traffic. This helps us maintain the quality of our free resources. manage your preferences below.
Detailed Cookie Preferences
This helps support our free resources through personalized marketing efforts and promotions.
Analytics cookies help us understand how visitors interact with our website, improving user experience and website performance.
Personalization cookies enable us to customize the content and features of our site based on your interactions, offering a more tailored experience.
