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Neuro-IG: A Hybrid System for Selection and Elimination of Predictor Variables and non Relevant Individuals
Volume 18, Issue 2 (2007), pp. 163–186
Baghdad Atmani   Bouziane Beldjilali  

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https://doi.org/10.15388/Informatica.2007.170
Pub. online: 1 January 2007      Type: Research Article     

Received
1 June 2006
Published
1 January 2007

Abstract

In this article we present the general architecture of a hybrid neuro-symbolic system for the selection and stepwise elimination of predictor variables and non-relevant individuals for the construction of a model. Our purpose is to design tools for extracting the relevant variables and the relevant individuals for an automatic training from data. The objective is to reduce the complexity of storage, therefore the complexity of calculation, and to gradually improve the performance of ordering, that is to say to arrive at a good quality training.

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Keywords
hybrid system neural network automatic training pruning symbolic system rule extraction

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INFORMATICA

  • Online ISSN: 1822-8844
  • Print ISSN: 0868-4952
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