Data Mining with Enhanced Neural Networks-CMMSE

Mingo López, Fernando de and Castellanos Peñuela, Angel and Martínez Blanco, Ana and Sotto, Arcadio (2013). Data Mining with Enhanced Neural Networks-CMMSE. "Journal of Mathematical Modelling and Algorithms in Operations Research", v. 12 (n. 3); pp. 277-290. ISSN 1570-1166. https://doi.org/10.1007/s10852-013-9216-x.

Description

Title: Data Mining with Enhanced Neural Networks-CMMSE
Author/s:
  • Mingo López, Fernando de
  • Castellanos Peñuela, Angel
  • Martínez Blanco, Ana
  • Sotto, Arcadio
Item Type: Article
Título de Revista/Publicación: Journal of Mathematical Modelling and Algorithms in Operations Research
Date: 2013
ISSN: 1570-1166
Volume: 12
Subjects:
Freetext Keywords: Artificial neural network, Rule extraction, Symbolic rules, Data mining.
Faculty: E.U. de Informática (UPM)
Department: Organización y Estructura de la Información [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Abstract This paper presents a new method to extract knowledge from existing data sets, that is, to extract symbolic rules using the weights of an Artificial Neural Network. The method has been applied to a neural network with special architecture named Enhanced Neural Network (ENN). This architecture improves the results that have been obtained with multilayer perceptron (MLP). The relationship among the knowledge stored in the weights, the performance of the network and the new implemented algorithm to acquire rules from the weights is explained. The method itself gives a model to follow in the knowledge acquisition with ENN.

More information

Item ID: 29102
DC Identifier: http://oa.upm.es/29102/
OAI Identifier: oai:oa.upm.es:29102
DOI: 10.1007/s10852-013-9216-x
Official URL: http://link.springer.com/journal/10852/12/3/page/1
Deposited by: Memoria Investigacion
Deposited on: 27 Jun 2014 10:52
Last Modified: 22 Sep 2014 11:44
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