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JACIII Vol.3 No.4 pp. 326-331
doi: 10.20965/jaciii.1999.p0326
(1999)

Paper:

Local Representation Neural Networks for Feature Selection

M. Mar Abad Grau* and L. Daniel Hernandez Molinero**

*Departamento de Lenguajes y Sistemas Informaticos Universidad de Granada, Espana

**Departamento de Informatica y Sistemas, Universidad de Murcia, Espana

Received:
June 25, 1998
Accepted:
May 19, 1999
Published:
August 20, 1999
Keywords:
Neural network, Superrised learning, Feature selection
Abstract

Pruning methods for feature selection in neural networks start out from the idea that the representation of the data must evolve from a distributed representation of the information to a more localised representation which will represent the skeleton of the network, needing long training times imposed by the back propagation algorithm. Even the quasi-Newton algorithm spent a long computation time. We propose a three-layer network based on local representation with a step-threshold function and an algorithm called Direct Method for Structural Learning, both allow a very fast pruning of superfluous attributes.

Cite this article as:
M. Mar Abad Grau and L. Daniel Hernandez Molinero, “Local Representation Neural Networks for Feature Selection,” J. Adv. Comput. Intell. Intell. Inform., Vol.3, No.4, pp. 326-331, 1999.
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