Selecting features in microarray classification using ROC curves Export

@article{citeulike:2799972, abstract = {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.}, author = {Mamitsuka, Hiroshi}, booktitle = {Bioinformatics}, citeulike-article-id = {2799972}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.patcog.2006.07.010}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V14-4KWCJSK-2/1/804962bf7c9a9efc029c7cd02039c45a}, doi = {10.1016/j.patcog.2006.07.010}, journal = {Pattern Recognition}, keywords = {feature-selection}, month = {December}, number = {12}, pages = {2393--2404}, posted-at = {2008-05-14 21:39:50}, priority = {5}, title = {Selecting features in microarray classification using ROC curves}, url = {http://dx.doi.org/10.1016/j.patcog.2006.07.010}, volume = {39}, year = {2006} }

TY - JOUR ID - citeulike:2799972 L3 - citeulike-article-id:2799972 N2 - 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. IS - 12 JF - Pattern Recognition EP - 2404 TI - Selecting features in microarray classification using ROC curves VL - 39 T2 - Bioinformatics SP - 2393 KW - feature-selection AU - Mamitsuka, Hiroshi PY - 2006/12// UR - http://dx.doi.org/10.1016/j.patcog.2006.07.010 ER -