A Dempster-Shafer theory of evidence approach for combining trained neural networks

Ahmed Al-Ani; Mohamed Deriche

doi:10.1109/ISCAS.2001.921429

A Dempster-Shafer theory of evidence approach for combining trained neural networks

Ahmed Al-Ani
, Mohamed Deriche

Queensland University of Technology

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods.

Original language	English
Pages (from-to)	703-706
Number of pages	4
Journal	Proceedings - IEEE International Symposium on Circuits and Systems
Volume	3
DOIs	https://doi.org/10.1109/ISCAS.2001.921429
State	Published - 2001
Externally published	Yes

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title = "A Dempster-Shafer theory of evidence approach for combining trained neural networks",

abstract = "The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods.",

author = "Ahmed Al-Ani and Mohamed Deriche",

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AU - Deriche, Mohamed

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AB - The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since there is not a unique way to perform such a combination, we have developed an algorithm which adapts to the training data set so that the overall mean square error is minimised. The proposed method was proved to be superior and more robust than other available combination methods.

UR - https://www.scopus.com/pages/publications/0035005408

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M3 - Article

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EP - 706

JO - Proceedings - IEEE International Symposium on Circuits and Systems

JF - Proceedings - IEEE International Symposium on Circuits and Systems

ER -

A Dempster-Shafer theory of evidence approach for combining trained neural networks

Abstract

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