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Data Mining with Enhanced Neural Networks-CMMSE
Cita
Mingo López, Fernando de; Castellanos Peñuela, Angel; Martínez Blanco, Ana y 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.
Descripción
| Título: | Data Mining with Enhanced Neural Networks-CMMSE |
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| Autor/es: |
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| Tipo de Documento: | Artículo |
| Título de Revista/Publicación: | Journal of Mathematical Modelling and Algorithms in Operations Research |
| Fecha: | 2013 |
| Volumen: | 12 |
| Materias: | |
| Palabras Clave Informales: | Artificial neural network, Rule extraction, Symbolic rules, Data mining. |
| Escuela: | E.U. de Informática (UPM) [antigua denominación] |
| Departamento: | Organización y Estructura de la Información [hasta 2014] |
| Licencias Creative Commons: | Reconocimiento - Sin obra derivada - No comercial |
Resumen
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.
Más información
| ID de Registro: | 29102 |
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| Identificador DC: | http://oa.upm.es/29102/ |
| Identificador OAI: | oai:oa.upm.es:29102 |
| Identificador DOI: | 10.1007/s10852-013-9216-x |
| URL Oficial: | http://link.springer.com/journal/10852/12/3/page/1 |
| Depositado por: | Memoria Investigacion |
| Depositado el: | 27 Jun 2014 10:52 |
| Ultima Modificación: | 22 Sep 2014 11:44 |
