DETERMINING INPUT FEATURES FOR MULTILAYER PERCEPTRONS

被引：102

作者：

BELUE, LM

BAUER, KW

机构：

[1] Department of Operational Sciences, Air Force Institute of Technology, United States Air Force

来源：

NEUROCOMPUTING | 1995年 / 7卷 / 02期

关键词：

MULTILAYER PERCEPTRON; FEATURE SELECTION; SALIENCY;

D O I：

10.1016/0925-2312(94)E0053-T

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A method of selecting salient features from a superset of features is developed. Two metrics are used to measure the average saliency of injected noise. A confidence interval constructed around this average allows for the identification of features that contribute little to classification. This feature selection method is applied to an exclusive-or (XOR) problem containing noise and a four-class problem. This method of determining input features is shown to result in more accurate, faster training multilayer perceptron classifiers.

引用

页码：111 / 121

页数：11