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


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

June 25, 1998
May 19, 1999
August 20, 1999
Neural network, Superrised learning, Feature selection
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. Grau and L. 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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