We describe a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE (Entropy-based Recursive Feature Elimination) 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
An accelerated procedure for recursive feature ranking on microarray data
Furlanello, Cesare;Serafini, Maria;Merler, Stefano;Jurman, Giuseppe
2003
Abstract
We describe a new wrapper algorithm for fast feature ranking in classification problems. The E-RFE (Entropy-based Recursive Feature Elimination) 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 dataI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
