Selecting features in microarray classification using ROC curves

Authors:
Hiroshi Mamitsuka
Affiliations:
Institute for Chemical Research, Kyoto University, Gokasho, Uji 611-0011, Japan
Venue:
Pattern Recognition
Year:
2006

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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.