title: Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression.
abstract: Building 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.
authors.affiliation: Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA. x1jiang@ucsd.edu
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Robert
authors.initials: R
authors.lastname: El-Kareh
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D001185:Artificial Intelligence; D004194:Disease / Q000145:classification*; D006801:Humans; D016015:Logistic Models; D055641:Mathematical Concepts; D012372:ROC Curve; D012680:Sensitivity and Specificity