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Proceedings Paper

A method for assessing the uncertainty in feature selection tasks
Author(s): Yulei Jiang
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Paper Abstract

Feature selection is a common task in the development of computer-aided diagnosis techniques and other areas of research where there is a need to identify discriminative variables that can be used to separate two classes, e.g., individuals with a certain type of disease and those without. Feature-selection results based on a small sample are not reliable if they are not reproducible in replicated experiments. However, when unlimited large samples are not available, it is often difficult to assess sample size with respect to the reliability of the results of feature selection. We propose a method that could be used for assessing the uncertainty in the results of feature selection. We compute a joint likelihood function of observed ROC data of multiple features of interest conditional on a joint ROC model for the multiple features. From the likelihood function, we compute the posterior distribution (i.e., relative probability) of competing joint ROC models of the multiple features for giving rise to the observed ROC data. The posterior distribution can be used for statistical inference of the performance of the multiple features.

Paper Details

Date Published: 17 March 2006
PDF: 5 pages
Proc. SPIE 6146, Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment, 614604 (17 March 2006); doi: 10.1117/12.657935
Show Author Affiliations
Yulei Jiang, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 6146:
Medical Imaging 2006: Image Perception, Observer Performance, and Technology Assessment
Yulei Jiang; Miguel P. Eckstein, Editor(s)

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