Quantification Classification Algorithm of Multiple Sources of Evidence

Abstract

Although Dempster-Shafer (D-S) evidence theory and its reasoning mechanism can deal with imprecise and uncertain information by combining cumulative evidences for changing prior opinions of new evidences, there is a de¯ciency in applying classical D-S evidence theory combination rule when con°ict evidence appear À À À con°ict evidence causes counter-intuitive results. To address this issue, alternative combination rules have been proposed for resolving the appeared con°icts of evidence. An underlying assumption is that con°ict evidences exist, which, however, is not always true. Moreover, it has been veri¯ed that con°ict factors may not be accurate to characterize the degree of con°ict. Instead, the Jousselme distance has been regarded as a quanti¯cation criterion for the degree of con°ict because of its promising properties. To avoid the counter-intuitive results, multiple sources of evidence should be classi¯ed rst. This paper proposes a novel algorithm to quantify the classi¯cation of multiple sources of evidence based on a core vector method, and the algorithm is further veri¯ed by two examples. This study also explores the relationship between complementary information and con°icting evidence and discusses the stochastic interpretation of basic probability assignment functions.

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

“…Schubert [23] developed a discounted proportion method for conflict management by exploring the relationship between complementary information and conflicting evidence. Yang et al [24] quantified the evidence classification based on a core vector method. Lin et al [25] proposed a weighted evidence combination based on the Mahalanobis distance function, which can efficiently solve high evidence conflicts.…”