Creating Prototypes for Fast Classification in Dempster-Shafer Clustering

TL;DR

This paper introduces a fast classification method for evidence in Dempster-Shafer theory using prototypes, enabling efficient real-time evidence classification without full clustering.

Contribution

The paper proposes a prototype-based classification approach that reduces computational complexity to O(M * N), independent of total evidence accumulated.

Findings

Achieves classification with complexity O(M * N) per evidence piece

Reduces computational load compared to full clustering methods

Parameters M and N are fixed and domain-dependent

Abstract

We develop a classification method for incoming pieces of evidence in Dempster-Shafer theory. This methodology is based on previous work with clustering and specification of originally nonspecific evidence. This methodology is here put in order for fast classification of future incoming pieces of evidence by comparing them with prototypes representing the clusters, instead of making a full clustering of all evidence. This method has a computational complexity of O(M * N) for each new piece of evidence, where M is the maximum number of subsets and N is the number of prototypes chosen for each subset. That is, a computational complexity independent of the total number of previously arrived pieces of evidence. The parameters M and N are typically fixed and domain dependent in any application.