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http://dx.doi.org/10.29220/CSAM.2019.26.2.079

Selection of markers in the framework of multivariate receiver operating characteristic curve analysis in binary classification  

Sameera, G (Department of Statistics, Pondicherry University)
Vishnu, Vardhan R (Department of Statistics, Pondicherry University)
Publication Information
Communications for Statistical Applications and Methods / v.26, no.2, 2019 , pp. 79-89 More about this Journal
Abstract
Classification models pertaining to receiver operating characteristic (ROC) curve analysis have been extended from univariate to multivariate setup by linearly combining available multiple markers. One such classification model is the multivariate ROC curve analysis. However, not all markers contribute in a real scenario and may mask the contribution of other markers in classifying the individuals/objects. This paper addresses this issue by developing an algorithm that helps in identifying the important markers that are significant and true contributors. The proposed variable selection framework is supported by real datasets and a simulation study, it is shown to provide insight about the individual marker's significance in providing a classifier rule/linear combination with good extent of classification.
Keywords
multivariate receiver operating characteristic curve; precision; stepwise algorithm; variable selection;
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