Abstract-This paper presents a novel adaptive synthetic (ADASYN) sampling approach for learning from imbalanced data sets. The essential idea of ADASYN is to use a weighted distribution for different minority class examples according to their level of difficulty in learning, where more synthetic data is generated for minority class examples that are harder to learn compared to those minority examples that are easier to learn. As a result, the ADASYN approach improves learning with respect to the data distributions in two ways: (1) reducing the bias introduced by the class imbalance, and (2) adaptively shifting the classification decision boundary toward the difficult examples. Simulation analyses on several machine learning data sets show the effectiveness of this method across five evaluation metrics.
Adaptive Synthetic (ADASYN) REF is based on the idea of adaptively generating minority data samples according to their distributions using K nearest neighbor.
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
Computer Science