Combining Evolving Neural Network Classifiers Using Bagging

Sunghwan Sohn, Cihan H. Dagli

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

The performance of the neural network classifier significantly depends on its architecture and generalization. It is usual to find the proper architecture by trial and error. This is time consuming and may not always find the optimal network. For this reason, we apply genetic algorithms to the automatic generation of neural networks. Many researchers have provided that combining multiple classifiers improves generalization. One of the most effective combining methods is bagging. In bagging, training sets are selected by resampling from the original training set and classifiers trained with these sets are combined by voting. We implement the bagging technique into the training of evolving neural network classifiers to improve generalization.

Original languageEnglish (US)
Pages3218-3222
Number of pages5
StatePublished - Sep 25 2003
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2003 - Portland, OR, United States
Duration: Jul 20 2003Jul 24 2003

Other

OtherInternational Joint Conference on Neural Networks 2003
CountryUnited States
CityPortland, OR
Period7/20/037/24/03

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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Sohn, S., & Dagli, C. H. (2003). Combining Evolving Neural Network Classifiers Using Bagging. 3218-3222. Paper presented at International Joint Conference on Neural Networks 2003, Portland, OR, United States.