Please use this identifier to cite or link to this item: https://doi.org/10.3109/1354750X.2013.868516
Title: Sorting multiple classes in multi-dimensional ROC analysis: Parametric and nonparametric approaches
Authors: Li, J. 
Chow, Y.
Wong, W.K.
Wong, T.Y.
Keywords: Hypervolume under the ROC manifold
Kruskal-Wallis test
Multivariate normal distribution
Relative effects
Volume under the ROC surface
Issue Date: Feb-2014
Citation: Li, J., Chow, Y., Wong, W.K., Wong, T.Y. (2014-02). Sorting multiple classes in multi-dimensional ROC analysis: Parametric and nonparametric approaches. Biomarkers 19 (1) : 1-8. ScholarBank@NUS Repository. https://doi.org/10.3109/1354750X.2013.868516
Abstract: In large-scale data analysis, such as in a microarray study to identify the most differentially expressed genes, diagnostic tests are frequently used to classify and predict subjects into their different categories. Frequently, these categories do not have an intrinsic natural order even though the quantitative test results have a relative order. As identifying the correct order for a proper definition of accuracy measures is important for a high-dimensional receiver operating characteristic (ROC) analysis, we propose rigorous and automated approaches to sort out the multiple categories using simple summary statistics such as means and relative effects. We discuss the hypervolume under the ROC manifold (HUM), its dependence on the order of the test results and the minimum acceptable HUM values in a general multi-category classification problem. Using a leukemia data set and a liver cancer data set, we show how our approaches provide accurate screening results when we have a large number of tests. © 2014 Informa UK Ltd. All rights reserved.
Source Title: Biomarkers
URI: http://scholarbank.nus.edu.sg/handle/10635/105504
ISSN: 1354750X
DOI: 10.3109/1354750X.2013.868516
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