Improved information fusion approach based on D-S evidence theory

Conventional D-S evidence theory has an unavoidable disadvantage in that it will give counter-intuitive result when fusing high conflict information. This paper proposes an improved method to solve this problem. By reassigning weight factors before fusing, the method can give reasonable results especially when the initial weight factors of conflict evidences are almost equal. It gives an adjustable factor to adjust the reassigning force. An example is given to illustrate these advantages.

[1]  John K Kruschke,et al.  Bayesian data analysis. , 2010, Wiley interdisciplinary reviews. Cognitive science.

[2]  Xianfeng Fan,et al.  Fault diagnosis of machines based on D-S evidence theory. Part 2: Application of the improved D-S evidence theory in gearbox fault diagnosis , 2006, Pattern Recognit. Lett..

[3]  Xiaohong Yuan,et al.  Engine fault diagnosis based on multi-sensor information fusion using Dempster-Shafer evidence theory , 2007, Inf. Fusion.

[4]  Éloi Bossé,et al.  A new distance between two bodies of evidence , 2001, Inf. Fusion.

[5]  G. Klir,et al.  Uncertainty-based information: Elements of generalized information theory (studies in fuzziness and soft computing). , 1998 .

[6]  Jerry Nedelman,et al.  Book review: “Bayesian Data Analysis,” Second Edition by A. Gelman, J.B. Carlin, H.S. Stern, and D.B. Rubin Chapman & Hall/CRC, 2004 , 2005, Comput. Stat..

[7]  Catherine K. Murphy Combining belief functions when evidence conflicts , 2000, Decis. Support Syst..

[8]  Xianfeng Fan,et al.  Fault diagnosis of machines based on D-S evidence theory. Part 1: D-S evidence theory and its improvement , 2006, Pattern Recognit. Lett..

[9]  Weiru Liu,et al.  Analyzing the degree of conflict among belief functions , 2006, Artif. Intell..

[10]  Hong-Zhong Huang,et al.  Fuzzy multi-objective optimization decision-making of reliability of series system , 1997 .

[11]  Ronald R. Yager,et al.  A class of fuzzy measures generated from a Dempster-Shafer belief structure , 1999, Int. J. Intell. Syst..

[12]  Eric Lefevre,et al.  Belief function combination and conflict management , 2002, Inf. Fusion.

[13]  R. Yager On the dempster-shafer framework and new combination rules , 1987, Inf. Sci..

[14]  Isabelle Bloch,et al.  Some aspects of Dempster-Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account , 1996, Pattern Recognit. Lett..

[15]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[16]  Michael J. Pont,et al.  Application of Dempster-Shafer theory in condition monitoring applications: a case study , 2001, Pattern Recognit. Lett..