An accelerated procedure for recursive feature ranking on microarray data

被引：56

作者：

Furlanello, C ^{[1
]}

Serafini, M ^{[1
]}

Merler, S ^{[1
]}

Jurman, G ^{[1
]}

机构：

[1] ITC Irst, I-38050 Trento, Italy

来源：

NEURAL NETWORKS | 2003年 / 16卷 / 5-6期

关键词：

entropy-based recursive feature elimination; microarray; support vector machines;

D O I：

10.1016/S0893-6080(03)00103-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2003 Elsevier Science Ltd. All rights reserved.

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收藏

页码：641 / 648

页数：8

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