Optimized consensus theory

Jon Atli Benediktsson*, Johannes R. Sveinsson, Philip H. Swain

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Statistical classification methods based on consensus from several data sources are considered. The methods need weighting mechanisms to control the influence of each data source in the combined classification. The weights are optimized in order to improve the combined classification accuracies. Both linear and non-linear methods are considered for the optimization. A non-linear method which utilizes a neural network is proposed and gives excellent results in experiments. Consensus theory optimized with neural networks outperforms all other methods both in terms of training and test accuracies in the experiments.

Original languageEnglish
Pages (from-to)3490-3493
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
Publication statusPublished - 1996
EventProceedings of the 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 6) - Atlanta, GA, USA
Duration: 7 May 199610 May 1996

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