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: Chapter in Book/Report/Conference proceeding › Conference contribution › 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
Title of host publication	ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings
Pages	703-706
Number of pages	4
DOIs	https://doi.org/10.1109/ISCAS.2001.921429
State	Published - 2001
Externally published	Yes
Event	2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001 - Sydney, NSW, Australia Duration: 6 May 2001 → 9 May 2001

Publication series

Name	ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings
Volume	3

Conference

Conference	2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001
Country/Territory	Australia
City	Sydney, NSW
Period	6/05/01 → 9/05/01

Access to Document

10.1109/ISCAS.2001.921429

Cite this

Al-Ani, A., & Deriche, M. (2001). A Dempster-Shafer theory of evidence approach for combining trained neural networks. In ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings (pp. 703-706). Article 921429 (ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings; Vol. 3). https://doi.org/10.1109/ISCAS.2001.921429

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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",

year = "2001",

doi = "10.1109/ISCAS.2001.921429",

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series = "ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings",

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note = "2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001 ; Conference date: 06-05-2001 Through 09-05-2001",

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Al-Ani, A & Deriche, M 2001, A Dempster-Shafer theory of evidence approach for combining trained neural networks. in ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings., 921429, ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings, vol. 3, pp. 703-706, 2001 IEEE International Symposium on Circuits and Systems, ISCAS 2001, Sydney, NSW, Australia, 6/05/01. https://doi.org/10.1109/ISCAS.2001.921429

A Dempster-Shafer theory of evidence approach for combining trained neural networks. / Al-Ani, Ahmed; Deriche, Mohamed.
ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings. 2001. p. 703-706 921429 (ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings; Vol. 3).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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