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The weight decay backpropagation for generalizations with missing values

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  • Amit Gupta
  • Monica Lam

Abstract

The purpose of this study is to investigate the generalization power of a modified backpropagation training algorithm referred to as "weight decay". In particular, we focus on the effect of the weight decay method on data sets with missing values. Three data sets with real missing values and three data sets with missing values created by randomly deleting attribute values are adopted as the test bank in this study. We first reconstruct missing values using four different methods, viz., standard backpropagation, iterative multiple regression, replacing by average, and replacing by zero. Then the standard backpropagation and the weight decay backpropagation are used to train networks for classification predictions. Experimental results show that the weight decay backpropagation can at least achieve a performance equivalent to the standard backpropagation. In addition, there is evidence that the standard backpropagation is a viable tool to reconstruct missing values. Experimental results also show that in the same data set, the higher the percentage of missing values, the higher the differential effects from reconstruction methods. Copyright Kluwer Academic Publishers 1998

Suggested Citation

  • Amit Gupta & Monica Lam, 1998. "The weight decay backpropagation for generalizations with missing values," Annals of Operations Research, Springer, vol. 78(0), pages 165-187, January.
    • Handle: RePEc:spr:annopr:v:78:y:1998:i:0:p:165-187:10.1023/a:1018945915940
    DOI: 10.1023/A:1018945915940
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    Cited by:

    1. Souad Larabi-Marie-Sainte & Roohi Jan & Ali Al-Matouq & Sara Alabduhadi, 2021. "The impact of timetable on student’s absences and performance," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    2. Giorgio Gnecco & Marcello Sanguineti, 2009. "The weight-decay technique in learning from data: an optimization point of view," Computational Management Science, Springer, vol. 6(1), pages 53-79, February.
    3. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.

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