Data mining with imbalanced class distributions: concepts and methods

By Ronaldo Cristiano Prati, Gustavo E. A. P. A. Batista, Maria Carolina Monard. 2009

67 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks

How researchers see this paper

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

In Evaluation & Benchmarks

1. Tristan Thrush
2. Douwe Kiela
3. Guillaume Lemaitre
4. Angelina McMillan-Major
5. Yacine Jernite
6. Alexandra Sasha Luccioni
7. Margaret Mitchell
8. Nazneen Rajani

Citations in the corpus

• Measuring Data (2022, background): “…extent to which the probability distribution of a variable deviates from the normal distribution) can help orient subsequent modeling approaches and indicate whether additional steps are necessary, for instance in terms of class balancing or data normalization (Prati et al., 2009; Provost, 2000).”
• Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning (2016, background): “4.4 Combination of over-and under-sampling SMOTE over-sampling can lead to over-fitting which can be avoided by applying cleaning under-sampling methods (Prati et al., 2009).”