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Selecting features in microarray classification using ROC curves
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DOI：10.1016/j.patcog.2006.07.010.png)
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We present a new method based on the ROC (Receiver Operating Characteristic) curve to efficiently select a feature subset in classifying a high-dimensional microarray dataset with a limited number of observations. Our method has two steps: (1) selecting the most relevant features to the target label using the ROC curve and (2) iteratively eliminating a redundant feature using the ROC curves. The ROC curve is strongly related with a non-parametric hypothesis testing, which must be effective for a dataset with small numerical observations. Experiments with real datasets revealed the significant performance advantage of our method over two competing feature subset selection methods. (c) 2006 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keyword:
feature subset selection
cDNA microarray
ROC (Receiver Operating Characteristic) curve
area between the ROC curve and the diagonal line (ARD)
area between the ROC curves (ABR)
non-parametric hypothesis testing
binary classification
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