An accelerated procedure for recursive feature ranking on microarray data

Authors:
C. Furlanello;M. Serafini;S. Merler;G. Jurman
Affiliations:
ITC-irst, v. Sommarive 18, Povo, I-38050 Trento, Italy;ITC-irst, v. Sommarive 18, Povo, I-38050 Trento, Italy;ITC-irst, v. Sommarive 18, Povo, I-38050 Trento, Italy;ITC-irst, v. Sommarive 18, Povo, I-38050 Trento, Italy
Venue:
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Year:
2003

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Abstract

We describe a new wrapper algorithm for fast feature ranking in classification problems. The Entropy-based Recursive Feature Elimination (E-RFE) method eliminates chunks of uninteresting features according to the entropy of the weights distribution of a SVM classifier. With specific regard to DNA microarray datasets, the method is designed to support computationally intensive model selection in classification problems in which the number of features is much larger than the number of samples. We test E-RFE on synthetic and real data sets, comparing it with other SVM-based methods. The speed-up obtained with E-RFE supports predictive modeling on high dimensional microarray data.