Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression

By Thomas Oommen, Laurie Gaskins Baise, Richard M. Vogel. 2011

134 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8e60c1597700d8b3d814cc91b0b0f8800aede4c0

How researchers see this paper

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

In Accounting

1. Giovanna Menardi
2. Nicola Torelli

In Artificial Intelligence

1. Klas Leino
2. Matt Fredrikson
3. Anupam Datta
4. Shayak Sen
5. Giovanna Menardi
6. Nicola Torelli
7. Emily Black
8. Su-In Lee

In Statistics and Probability

1. Shayak Sen
2. Matt Fredrikson
3. Anupam Datta
4. Giovanna Menardi
5. Nicola Torelli

Citations in the corpus

• Training and assessing classification rules with imbalanced data (2012, methodology): “Some examples are provided by the recent applications of Ström and Koker (2011); Percannella et al. (2011), and (Pavón et al. 2011).”
• Feature-Wise Bias Amplification (2018, background): “There are a myriad of empirical investigations of the effects of class imbalance in machine learning and different ways of mitigating these effects (Maloof, 2003; Chawla, 2005; Mazurowski et al., 2008; Oommen et al., 2011; Wallace et al., 2011b).”