Cluster-based under-sampling approaches for imbalanced data distributions

By Show-Jane Yen, Yue-Shi Lee. Expert systems with applications, 2009

740 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

ELF: An Early-Exiting Framework for Long-Tailed Classification (2020, background): “For example, SMOTE [4] oversamples the minority class through linear interpolation, whereas [16] undersamples by clustering the majority class examples and replacing them with a few anchor points.”
Borderline over-sampling for imbalanced data classification (2009, methodology): “Other methods perform a clustering of the whole training set, and then apply a certain sampling technique to the resulting clusters that may consist of both the minority and majority instances (Cieslak and Chawla, 2008; Yen and Lee, 2009).”
Striking the Right Balance With Uncertainty (2019, neutral): “Clustering [59] 75.”
Classification with class imbalance problem: A review (2015, neutral): “the research in [59] that use clustering to choose representatives training examples to gain better accuracy prediction for minority class.”
A Survey of Predictive Modeling on Imbalanced Domains (2016, neutral): “Nevertheless, SMOTE generates synthetic examples with the positive class label, disregarding the negative class examples that may lead to overgeneralization [Yen and Lee 2006; Maciejewski and Stefanowski 2011; Yen and Lee 2009].”