Data Mining with Enhanced Neural Networks-CMMSE

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• 作者：Ana Martínez Blanco…
• 关键词：Artificial neural networks ; Rule extraction ; Symbolic rules ; Data mining ; 68Q32 ; 62M45 ; 62J05
• 刊名：Journal of Mathematical Modelling and Algorithms
• 出版年：2013
• 出版时间：September 2013
• 年：2013
• 卷：12
• 期：3
• 页码：277-290
• 全文大小：603KB
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• 作者单位：Ana Martínez Blanco (1)
  Angel Castellanos Pe?uela (1)
  Luis Fernando de Mingo López (2)
  Arcadio Sotto (3)
  
  1. Basic Sciences Applied to Forestry Engineering, Technical University of Madrid (UPM), Av. Ramiro de Maeztu s/n, 28040, Madrid, Spain
  2. Organization and Information Structure, Technical University of Madrid (UPM), Crta. de Valencia km. 7, 28031, Madrid, Spain
  3. Chemical and Environmental Technology, ESCET Universidad Rey Juan Carlos, Calle Tulipan s/n, 28993, Mostoles, Madrid, Spain
  
• ISSN：1572-9214

文摘

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.