Infinitely Imbalanced Logistic Regression

By Art B. Owen. 2007

125 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.

Citations in the corpus

Relative Performance Evaluation and Related Peer Groups in Executive Compensation Contracts (2011, unknown): “Given that the relative proportion of selected versus unselected RPE peers is highly unbalanced, the estimated coefficients from logistic regressions could be significantly biased and inefficient (Owen 2007).”
Object-oriented mapping of landslides using Random Forests (2011, background): “Brown dot: Highest ranked texture measure with the respective rank. tion scales. class imbalance (Owen, 2007).”
Correction note: Poisson point process models solve the “pseudo-absence problem” for presence-only data in ecology (2010, background): “Owen (2007) also considered the logistic regression setting where the number of presence points is ﬁxed, and the number of pseudo-absences increases to inﬁnity, and referred to this as “inﬁnitely unbalanced logistic regression.””
An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons (2008, background): “Although the employment of many data sets could seem beneficial in order to improve the generalization of results, in some specific domains, that is, imbalanced classification (Owen, 2007) or multi-instance classification (Murray et al., 2005), data sets are difficult to find.”
Simple and Scalable Response Prediction for Display Advertising (2014, methodology): “Logistic regression with unbalanced classes has been well studied in the literature; more details can be found in [King and Zeng 2001; Owen 2007].”