Neuro-IG: A Hybrid System for Selection and Elimination of Predictor Variables and non Relevant Individuals

  • Authors:
  • Baghdad Atmani_aff1n2;Bouziane Beldjilali

  • Affiliations:
  • af1 Leibniz Laboratory, IMAG, 46 Av Felix Viallet, 38031 Cedex Grenoble, France, e-mail: baghdad.atmani@imag.fr;af2 Department of Computer Science, Faculty of Science, University of Oran, BP 1524 El M'Naouer 31000 Oran, Algeria, e-mail: baghdad.atmani@univ-oran.dz, bouziane.beldjilali@univ-oran.dz

  • Venue:
  • Informatica
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.