Training and assessing classification rules with imbalanced data

By Giovanna Menardi, Nicola Torelli. Data mining and knowledge discovery, 2012

738 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Health Information Management

View Paper: https://www.semanticscholar.org/paper/1adb8023b571e3d7ba5720083259d25f7e658eef

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

1. Francisco Herrera
2. Alberto Fernández
3. Salvador Garcia
4. Luís Torgo
5. Paula Branco
6. Rita P. Ribeiro
7. Nitesh V. Chawla
8. Chakkrit Kla Tantithamthavorn

In Health Information Management

1. Ahmed E. Hassan
2. Ken-ichi Matsumoto
3. Paula Branco
4. Chakkrit Kla Tantithamthavorn
5. Nicola Lunardon
6. Tarid Wongvorachan
7. Rita P. Ribeiro
8. Surina He

Citations in the corpus

• Plankton classification on imbalanced large scale database via convolutional neural networks with transfer learning (2016, background): “This class-imbalance problem is known to cause a classifier to be biased so that its performance is degraded for small-sized classes [6, 7].”
• SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary (2018, methodology): “• ROSE (Menardi & Torelli, 2014): ROSE is an oversampling technique proposed within a complete framework to obtain classification rules in imbalanced data.”
• ROSE: a Package for Binary Imbalanced Learning (2014, methodology): “It can be easily shown that, given selection of the class label Yj, the generation of new examples from Yj, according to ROSE, corresponds to the generation of data from the kernel density estimate of f (x|Yj), with kernel K and smoothing matrix Hj (Menardi and Torelli, 2014).”
• A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Another framework, named Random Over Sampling Examples (ROSE), for dealing with the problem of imbalanced classification was presented by Menardi and Torelli [2010] based on a smoothed bootstrap re-sampling technique.”
• A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, methodology): “However, by reducing the size of the dataset, RUS may exclude important information and patterns that could be helpful for the prediction and, consequently, reduce the predictive accuracy of the algorithm [28,31].”
• The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models (2018, methodology): “Thus, we select the ROSE technique [44], [45] in our study.”