An efficient triplet-based algorithm for evidential reasoning

Yaxin Bi

doi:10.1002/int.20278

Return

An efficient triplet-based algorithm for evidential reasoning

2008-01-01

Yaxin Bi *

DOI：10.1002/int.20278

Original

Save

View PDF

Abstract

En 中文

Linear-time computational techniques based on the structure of an evidence space have been developed for combining multiple pieces of evidence using Dempster's rule (orthogonal sum), which is available on a number of contending hypotheses. They offer a means of making the computation-intensive calculations involved more efficient in certain circumstances. Unfortunately, they restrict the orthogonal sum of evidential functions to the dichotomous structure that applies only to elements and their complements. In this paper, we present a novel evidence structure in terms of a triplet and a set of algorithms for evidential reasoning. The merit of this structure is that it divides a set of evidence into three subsets, distinguishing the trivial evidential elements from the important ones-focusing particularly on some elements of an evidence space. It avoids the deficits of the dichotomous structure in representing the preference of evidence and estimating the basic probability assignment of evidence. We have established a formalism for this structure and the general formulae for combining pieces of evidence in the form of the triplet, which have been theoretically and empirically justified. (C) 2008 Wiley Periodicals, Inc.

Keywords:

RULE

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.