Fast Dempster-Shafer clustering using a neural network structure

TL;DR

This paper introduces a neural network-based method for clustering evidence in Dempster-Shafer theory, offering faster performance and scalability over traditional iterative optimization, especially for large-scale problems.

Contribution

The paper presents a novel neural network approach for Dempster-Shafer clustering that reduces computational complexity and improves efficiency for large problems.

Findings

Neural network method is faster than iterative optimization for large problems.

The neural structure can find a global minimum in multiple runs for up to six clusters.

Metaconflict increases faster with neural networks but remains moderate per cluster and evidence.

Abstract

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.