Dynamically weighted ensemble neural networks for classification

Daniel Jimenez, Nicolas Walsh

Research output: Contribution to conferencePaperpeer-review

87 Scopus citations

Abstract

Combining the outputs of several neural networks into an aggregate output often gives improved accuracy over any individual output. The set of networks is known as an ensemble or committee. This paper presents an ensemble method for classification that has advantages over other techniques for linear combining. Normally, the output of an ensemble is a weighted sum whose are weights fixed having been determined from the training or validation data. Our ensembles are weighted dynamically, the weights determined from the respective certainties of the network outputs. The more certain a network seems to be of its decision, the higher the weight.

Original languageEnglish (US)
Pages753-756
Number of pages4
StatePublished - Jan 1 1998
EventProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
Duration: May 4 1998May 9 1998

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period5/4/985/9/98

ASJC Scopus subject areas

  • Software

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