ADASYN, or Adaptive Synthetic Sampling Approach for Imbalanced Learning, is a data resampling technique utilized in machine learning to address imbalanced datasets, where the number of instances in one class significantly outweighs others. In the context of crypto trading or risk models, it is applied to enhance the performance of algorithms by generating synthetic data points for minority classes, such as rare fraud events or specific market anomalies. This process improves the model’s ability to learn and detect infrequent but critical occurrences.
Mechanism
The mechanism of ADASYN operates by identifying minority class samples that are harder to classify based on their proximity to majority class instances. For each such minority sample, synthetic samples are generated in its vicinity, with the quantity of synthetic data inversely proportional to the density of existing minority samples in that region. This adaptive approach strategically biases the data generation towards regions where classification difficulty is higher, effectively expanding the decision boundary for minority classes.
Methodology
The methodology involves several steps: first, calculating the imbalance ratio for the dataset; second, for each minority class instance, determining its k-nearest neighbors; third, identifying the number of synthetic samples to create for each minority instance based on the ratio of majority to minority neighbors; and finally, generating these synthetic samples along the line segments connecting the chosen minority instance to its minority neighbors, introducing a small random perturbation. This systematic augmentation ensures a more balanced and representative dataset for training predictive models.
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