AMIA Annu Symp Proc - Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression.

Tópicos

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Resumo

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.

Resumo Limpo

build classifi medic problem often involv deal rare import event imbalanc dataset pose challeng ordinari classif algorithm logist regress lr support vector machin svm lack effect strategi deal imbalanc train data often result model exhibit poor discrimin propos novel approach estim class membership base evalu pairwis relationship train data method propos pairwis expand logist regress improv discrimin higher accuraci compar exist method two imbalanc dataset thus show promis potenti remedi problem

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