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Base belief function: an efficient method of conflict management

  • Yunjuan Wang
  • Kezhen Zhang
  • Yong DengEmail author
Original Research

Abstract

Dempster–Shafer evidence theory is widely used in many applications such as decision making and pattern recognition. However, Dempster’s combination rule often produces results that do not reflect the actual distribution of belief when collected evidence highly conflicts each other. In this paper, a base belief function is proposed to modify the classical basic probability assignment before combination in closed-world. Base belief function focuses on making combination result intuitive especially when evidences highly conflict each other. Compared to other methods, the combination result produced by proposed method is logical and consistent with real world with less computational complexity and better performance. The advantage of base belief function is that it can avoid high conflicts between evidences and is especially suitable for the situation where the evidences appear in sequence. Several numerical examples as well as experiments using real data sets from the UCI machine learning repository for classification are employed to verify the rationality of the proposed method.

Keywords

Dempster–Shafer evidence theory Belief function Conflicting evidence Base belief function 

Notes

Acknowledgements

The authors greatly appreciate the reviews’ suggestions and the editor’s encouragement. This work is partially supported by National Natural Science Foundation of China (Grant Nos. 61573290 and 61503237).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Fundamental and Frontier ScienceUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.The School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina

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