ADASYN: Adaptive synthetic sampling approach for imbalanced learning. 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.

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  1. Loynes, Christopher; Ouenniche, Jamal; De Smedt, Johannes: The detection and location estimation of disasters using Twitter and the identification of non-governmental organisations using crowdsourcing (2022)
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  10. Tao, Xinmin; Li, Qing; Guo, Wenjie; Ren, Chao; He, Qing; Liu, Rui; Zou, JunRong: Adaptive weighted over-sampling for imbalanced datasets based on density peaks clustering with heuristic filtering (2020)
  11. Geng, Kunling; Shin, Dae C.; Song, Dong; Hampson, Robert E.; Deadwyler, Samuel A.; Berger, Theodore W.; Marmarelis, Vasilis Z.: Multi-input, multi-output neuronal mode network approach to modeling the encoding dynamics and functional connectivity of neural systems (2019)
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