ADASYN

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.


References in zbMATH (referenced in 19 articles )

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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)
  2. Barella, Victor H.; Garcia, Luís P. F.; de Souto, Marcilio C. P.; Lorena, Ana C.; de Carvalho, André C. P. L. F.: Assessing the data complexity of imbalanced datasets (2021)
  3. Bernardo, Alessio; Della Valle, Emanuele: VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams (2021)
  4. Chen, Zhi; Duan, Jiang; Kang, Li; Qiu, Guoping: A hybrid data-level ensemble to enable learning from highly imbalanced dataset (2021)
  5. Koziarski, Michał; Bellinger, Colin; Woźniak, Michał: RB-CCR: radial-based combined cleaning and resampling algorithm for imbalanced data classification (2021)
  6. Pereira, Rodolfo M.; Costa, Yandre M. G.; Silla, Carlos N. Jr.: Handling imbalance in hierarchical classification problems using local classifiers approaches (2021)
  7. Shahee, Shaukat Ali; Ananthakumar, Usha: An overlap sensitive neural network for class imbalanced data (2021)
  8. Soltanzadeh, Paria; Hashemzadeh, Mahdi: RCSMOTE: range-controlled synthetic minority over-sampling technique for handling the class imbalance problem (2021)
  9. Steininger, Michael; Kobs, Konstantin; Davidson, Padraig; Krause, Anna; Hotho, Andreas: Density-based weighting for imbalanced regression (2021)
  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)
  12. Kocheturov, Anton; Pardalos, Panos M.; Karakitsiou, Athanasia: Massive datasets and machine learning for computational biomedicine: trends and challenges (2019)
  13. Lai, Chun Sing; Tao, Yingshan; Xu, Fangyuan; Ng, Wing W. Y.; Jia, Youwei; Yuan, Haoliang; Huang, Chao; Lai, Loi Lei; Xu, Zhao; Locatelli, Giorgio: A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty (2019)
  14. Xindi Wang, Onur Varol, Tina Eliassi-Rad: L2P: An Algorithm for Estimating Heavy-tailed Outcomes (2019) arXiv
  15. Yan, Yuan Ting; Wu, Zeng Bao; Du, Xiu Quan; Chen, Jie; Zhao, Shu; Zhang, Yan Ping: A three-way decision ensemble method for imbalanced data oversampling (2019)
  16. Bellinger, Colin; Drummond, Christopher; Japkowicz, Nathalie: Manifold-based synthetic oversampling with manifold conformance estimation (2018)
  17. Vanhoeyveld, Jellis; Martens, David: Imbalanced classification in sparse and large behaviour datasets (2018)
  18. Koziarski, Michał; Wożniak, Michał: CCR: a combined cleaning and resampling algorithm for imbalanced data classification (2017)
  19. López, Victoria; Fernández, Alberto; García, Salvador; Palade, Vasile; Herrera, Francisco: An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics (2013) ioport