Ensemble of Evolving Neural Networks in Classification
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.
Recommended Citation
S. Sohn and C. H. Dagli, "Ensemble of Evolving Neural Networks in Classification," Neural Processing Letters, Springer Verlag, Jun 2004.
The definitive version is available at https://doi.org/10.1023/B:NEPL.0000035600.72270.f3
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Classification; Combining Classifiers; Data Mining; Genetic Algorithms; Neural Networks
International Standard Serial Number (ISSN)
1370-4621
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2004 Springer Verlag, All rights reserved.
Publication Date
01 Jun 2004
