Self-adaptive combination method for temporal evidence based on negotiation strategy

Abstract

In temporal information fusion, the information collected by sensors is obtained dynamically with the passage of time. Unlike the spatial information fusion, temporal fusion should be dynamic. Evidence theory has been applied to fuse temporal and spatial information; however, existing temporal fusion methods do not treat conflicting and non-conflicting evidence sources distinctively. Moreover, unlike spatial evidence sources, which are obtained simultaneously, temporal evidence sources cannot be evaluated simultaneously to obtain their degree of reliability. Thus, it is necessary to develop a method for temporal evidence combination. In this paper, a self-adaptive combination method for temporal evidence is proposed based on the negotiation strategy. In the proposed method, a set called an evidence set is constructed by the cumulative temporal fusion results of the previous moment, current moment, and future moment. The evidence set is evaluated as conflicting or non-conflicting according to the maximum power pignistic probability distance between each pair of evidence sources in the set. Then, temporal evidence sources are self-adaptively combined by different methods according to the degree of conflict. Numerical experiments were conducted to evaluate the performance of the proposed method. The results indicated that the proposed method is sufficiently effective and robust to support decision making.

Distance-Based Knowledge Measure for Intuitionistic Fuzzy Sets with Its Application in Decision Making

“…We know that similarity measure and distance measure are important in the research of fuzzy set theory [ 39 ]. Similarly, the construction of similarity measure and distance measures for AIFSs plays an important role in AIFSs [ 23 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ], and they are helpful for the comparison of intuitionistic fuzzy information [ 24 , 25 ].…”

Conflicting management of evidence combination from the point of improvement of basic probability assignment

“…However, in the framework of D‐S evidence theory, the classic Dempster's combination rule fails to fuse information correctly when facing highly conflicting evidence 50 . Hence, there are many studies that focus on how to fuse conflicting evidence correctly 51 – 57 …”

“…50 Hence, there are many studies that focus on how to fuse conflicting evidence correctly. [51][52][53][54][55][56][57] To sum up, these solutions can be generally divided into two categories, namely, refining data used in combination and altering the rules of combination. In the first category, the typical examples are Murphy's average approach 58 and Deng et al's improved method 59 which inherits from Murphy's method and further develops it.…”

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

“…Additionally, the Dempster rule of combination (DRC) in DSET can fuse multisource information to reduce uncertainty in the fusion process for supporting decision-making well [ 13 , 14 , 15 ]. Meanwhile, the DRC meets commutative and associative laws [ 16 , 17 ]. Hence, DSET has been extensively researched, including the aspects of D numbers [ 18 , 19 ], evidential reasoning [ 20 ], heuristic representation learning [ 21 ], entropy [ 22 , 23 ], generation [ 24 , 25 ], dependency [ 26 ], the negation [ 27 ] of BBAs, etc.…”