Feature analysis: Neural network and fuzzy set theoretic approaches

被引：66

作者：

De, RK ^{[1
]}

Pal, NR ^{[1
]}

Pal, SK ^{[1
]}

机构：

[1] INDIAN STAT INST,MACHINE INTELLIGENCE UNIT,CALCUTTA 700035,W BENGAL,INDIA

来源：

PATTERN RECOGNITION | 1997年 / 30卷 / 10期

关键词：

feature ranking; feature selection; neural networks; fuzziness measure;

D O I：

10.1016/S0031-3203(96)00190-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper a new scheme of feature ranking and hence feature selection using a Multilayer Perceptron (MLP) Network has been proposed. The novelty of the proposed MLP-based scheme and its difference from another MLP-based feature ranking scheme have been analyzed. In addition we have modified an existing feature ranking/selection scheme based on fuzzy entropy. Empirical investigations show that the proposed MLP-based scheme is superior to the other schemes implemented. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.

引用

页码：1579 / 1590

页数：12

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