Improving predictions in imbalanced data using pairwise expanded logistic regression.

TitleImproving predictions in imbalanced data using pairwise expanded logistic regression.
Publication TypeConference Proceedings
Year of Publication2011
AuthorsJiang, X, El-Kareh, R, Ohno-Machado, L
Conference NameAMIA Annu Symp Proc
Volume2011
Pagination625-34
Date Published2011
ISBN1942-597X
iDASH CategoryStatistics
AbstractBuilding classifiers for medical problems often involves dealing with rare, but important events. Imbalanced datasets pose challenges to ordinary classification algorithms such as Logistic Regression (LR) and Support Vector Machines (SVM). The lack of effective strategies for dealing with imbalanced training data often results in models that exhibit poor discrimination. We propose a novel approach to estimate class memberships based on the evaluation of pairwise relationships in the training data. The method we propose, Pairwise Expanded Logistic Regression, improved discrimination and had higher accuracy when compared to existing methods in two imbalanced datasets, thus showing promise as a potential remedy for this problem.
PubMed ID22195118