Neural-network-based metamodeling for parallel data mining

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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  • Lean Yu
  • Shouyang Wang
  • Kin Keung Lai

Related Research Unit(s)


Original languageEnglish
Pages (from-to)712-721
Journal / PublicationDynamics of Continuous, Discrete and Impulsive Systems Series B: Applications and Algorithms
Publication statusPublished - Aug 2007


Title1st International Conference Bio-Inspired Computing -Theory and Applications
Period18 - 22 September 2006


In this study, we discuss the use of supervised neural networks as a metamodeling technique to design a practical and powerful parallel data mining system. First of all, a bagging sampling technique is used to generate different training sets. Based on the different training sets, the different neural network models with various training sets are then trained to formulate different data mining models, i.e., base mining models. Finally, a metamodel can be produced by learning from all base mining models with neural-network-based metalearning strategy. For verification, a practical data mining experiment is performed for illustration purpose.

Research Area(s)

  • neural networks, metalearning, parallel data mining, metamodel, bagging, MULTILAYER FEEDFORWARD NETWORKS, SUPPORT VECTOR MACHINES, FOREIGN-EXCHANGE RATES, ENSEMBLE MODEL, FRAMEWORK