Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier]

By M. Santos, J. Soares, P. Abreu, Helder Araújo, João A. M. Santos. IEEE Computational Intelligence Magazine, 2018

317 citations recorded by the source corpus.

Although cross-validation is a standard procedure for performance evaluation, its joint application with oversampling remains an open question for researchers farther from the imbalanced data topic. A frequent experimental flaw is the application of oversampling algorithms to the entire dataset, resulting in biased models and overly-optimistic estimates.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b059e8a55c449ebec38305b72e5f7684e1620c6d

How researchers see this paper

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

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review (2023, background): “Cross Validation has distinctive characteristics, such as the fact that different folds have various efficacy, so it is possible to estimate how well a proposed model will perform overall, and overfitting can be eliminated using this technique [152].”
How Complex Is Your Classification Problem? (2018, background): “Other works in the analysis of imbalanced classification problems include Xing et al. (2013), Anwar et al. (2014), Santos et al. (2018), and Zhang et al. (2019).”