Fast Dempster-Shafer clustering using a neural network structure

Details Fast Dempster-Shafer clustering using a neural network structure Fast Dempster-Shafer clustering using a neural network structure

2003-05-16

arxiv.org/abs/cs/0305023v1

in Proceedings of the Seventh International Conference on Information Processing and Management of Uncertainty in Knowledge-ba

Computer Science

Artificial Intelligence

8 pages, 7 figures

Scientific paper

In this paper we study a problem within Dempster-Shafer theory where 2**n - 1 pieces of evidence are clustered by a neural structure into n clusters. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. However, for large scale problems we need a method with lower computational complexity. The neural structure was found to be effective and much faster than iterative optimization for larger problems. While the growth in metaconflict was faster for the neural structure compared with iterative optimization in medium sized problems, the metaconflict per cluster and evidence was moderate. The neural structure was able to find a global minimum over ten runs for problem sizes up to six clusters.

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