Sampling Bias and Class Imbalance in Maximum-likelihood Logistic Regression

• Published in: Mathematical geosciences Vol. 43; no. 1; pp. 99 - 120
• Main Authors: Oommen, Thomas, Baise, Laurie G., Vogel, Richard M.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 2011; Springer; Springer Nature B.V
• Subjects: Chemistry and Earth Sciences; Computer Science; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Exact sciences and technology; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Logistics; Natural hazards: prediction, damages, etc; Physics; Regression analysis; Statistical methods; Statistics for Engineering; Under-sampling; Over-sampling; Probabilistic classification; Imbalance; Sampling; Binary class; experimental studies; algorithms; models; probability; sampling; regression analysis; classification; mathematical methods; maximum likelihood; statistical analysis; Natural hazards
• ISSN: 1874-8961; 1874-8953
• DOI: 10.1007/s11004-010-9311-8

Logistic regression is a widely used statistical method to relate a binary response variable to a set of explanatory variables and maximum likelihood is the most commonly used method for parameter estimation. A maximum-likelihood logistic regression (MLLR) model predicts the probability of the event from binary data defining the event. Currently, MLLR models are used in a myriad of fields including geosciences, natural hazard evaluation, medical diagnosis, homeland security, finance, and many others. In such applications, the empirical sample data often exhibit class imbalance, where one class is represented by a large number of events while the other is represented by only a few. In addition, the data also exhibit sampling bias, which occurs when there is a difference between the class distribution in the sample compared to the actual class distribution in the population. Previous studies have evaluated how class imbalance and sampling bias affect the predictive capability of asymptotic classification algorithms such as MLLR, yet no definitive conclusions have been reached. We hypothesize that the predictive capability of the model is related to the sampling bias associated with the data so that the MLLR model has perfect predictability when the data have no sampling bias. We test our hypotheses using two simulated datasets with class distributions that are 50:50 and 80:20, respectively. We construct a suite of controlled experiments by extracting multiple samples with varying class imbalance and sampling bias from the two simulated datasets and fitting MLLR models to each of these samples. The experiments suggest that it is important to develop a sample that has the same class distribution as the original population rather than ensuring that the classes are balanced. Furthermore, when sampling bias is reduced either by using over-sampling or under-sampling, both sampling techniques can improve the predictive capability of an MLLR model.