A New Technique for Combining Multiple Classifiers Using the Dempster-Shafer Theory of Evidence
Abstract
This paper presents a new classifier combination technique based on the Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classifier combination methods when tested on three different classification problems.
Cite
Text
Al-Ani and Deriche. "A New Technique for Combining Multiple Classifiers Using the Dempster-Shafer Theory of Evidence." Journal of Artificial Intelligence Research, 2002. doi:10.1613/JAIR.1026Markdown
[Al-Ani and Deriche. "A New Technique for Combining Multiple Classifiers Using the Dempster-Shafer Theory of Evidence." Journal of Artificial Intelligence Research, 2002.](https://mlanthology.org/jair/2002/alani2002jair-new/) doi:10.1613/JAIR.1026BibTeX
@article{alani2002jair-new,
title = {{A New Technique for Combining Multiple Classifiers Using the Dempster-Shafer Theory of Evidence}},
author = {Al-Ani, Ahmed and Deriche, Mohamed A.},
journal = {Journal of Artificial Intelligence Research},
year = {2002},
pages = {333-361},
doi = {10.1613/JAIR.1026},
volume = {17},
url = {https://mlanthology.org/jair/2002/alani2002jair-new/}
}