Wed, 20 Aug 2008 22:08:47 BST CiteULike: bigga Mamitsuka http://www.citeulike.org/user/bigga/author/Mamitsuka CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/bigga/article/2799972 <i>Pattern Recognition, Vol. 39, No. 12. (December 2006), pp. 2393-2404.</i><br /><br />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. Selecting features in microarray classification using ROC curves Hiroshi Mamitsuka doi:10.1016/j.patcog.2006.07.010 Pattern Recognition, Vol. 39, No. 12. (December 2006), pp. 2393-2404. 2008-05-14T20:39:30-00:00 2006 Pattern Recognition 39 12 2393 2404 feature-selection