Ensemble of Evolving Neural Networks in Classification

Authors:
Sunghwan Sohn;Cihan H. Dagli
Affiliations:
Department of Engineering Management, Smart Engineering Systems Laboratory, University of Missouri-Rolla, MO 65409, USA/ e-mail: ssohn@umr.edu;Department of Engineering Management, Smart Engineering Systems Laboratory, University of Missouri-Rolla, MO 65409, USA/ e-mail: dagli@umr.edu
Venue:
Neural Processing Letters
Year:
2004

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Abstract

The ensemble of evolving neural networks, which employs neural networks and genetic algorithms, is developed for classification problems in data mining. This network meets data mining requirements such as smart architecture, user interaction, and performance. The evolving neural network has a smart architecture in that it is able to select inputs from the environment and controls its topology. A built-in objective function of the network offers user interaction for customized classification. The bagging technique, which uses a portion of the training set in multiple networks, is applied to the ensemble of evolving neural networks in order to improve classification performance. The ensemble of evolving neural networks is tested by various data sets and produces better performance than both classical neural networks and simple ensemble methods.