Fast Dempster-Shafer clustering using a neural network structure

In this article 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.

• Publication year

  2003

• Venue

  arXiv.org

• Publication date

  2003-05-16

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.1007/978-1-4615-5209-3_33arXiv cs/0305026
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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