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Determining the relevance of input features for multilayer perceptrons

机译:确定多层感知器的输入特征的相关性

摘要

This paper presents an approach to determine the relevance of individual input attributes for trained Multilayer Perceptrons (MLPs). To reflect the impact of an input attribute on the output of an MLP, the relevance is aimed at representing the output sensitivity of the MLP to the attribute variation. The sensitivity is defined as the mathematical expectation of output deviations of an MLP due to its input deviation with respect to overall input patterns. The basic idea for the introduction of such a relevance measure is that a well-trained MLP can capture salient features of the problem it deals with and thus become more sensitive to those input attributes that make more contributions to the MLP's behavior. The relevance can be employed as a relative criterion for assessing individual input attributes. The results from the experiments on two typical problems demonstrate the effectiveness of the relevance in identifying irrelevant input attribute.
机译:本文提出了一种确定受过训练的多层感知器(MLP)的各个输入属性的相关性的方法。为了反映输入属性对MLP输出的影响,相关性旨在表示MLP对属性变化的输出敏感性。灵敏度定义为由于MLP相对于整体输入模式的输入偏差而导致的输出偏差的数学期望。引入这种相关性度量的基本思想是,训​​练有素的MLP可以捕获所处理问题的显着特征,从而对那些对MLP行为做出更多贡献的输入属性变得更加敏感。相关性可以用作评估单个输入属性的相对标准。来自两个典型问题的实验结果证明了相关性在识别不相关输入属性方面的有效性。

著录项

  • 作者

    Zeng X; Huang Y; Yeung DS;

  • 作者单位
  • 年度 2003
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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