An improved measure for belief structure in the evidence theory

Qiang Zhang; Hao Li; Rongfei Li; Yongchuan Tang

doi:10.7717/peerj-cs.710

An improved measure for belief structure in the evidence theory

PeerJ Comput Sci. 2021 Sep 24:7:e710. doi: 10.7717/peerj-cs.710. eCollection 2021.

Authors

Qiang Zhang¹, Hao Li², Rongfei Li³, Yongchuan Tang⁴

Affiliations

¹ School of Automation, Chongqing University, Chongqing, Chongqing, China.
² School of Physics, Chongqing University, Chongqing, Chongqing, China.
³ School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, Chongqing, China.
⁴ School of Big Data and Software Engineering, Chongqing University, Chongqing, China.

Abstract

Dempster-Shafer evidence theory (D-S theory) is suitable for processing uncertain information under complex circumstances. However, how to measure the uncertainty of basic probability distribution (BPA) in D-S theory is still an open question. In this paper, a method of measuring total uncertainty based on belief interval distance is proposed. This method is directly defined in the D-S theoretical framework, without the need of converting BPA into probability distribution by Pignistic probability transformation. Thus, it avoids the loss of information. This paper analyzes the advantages and disadvantages of the previous total uncertainty of measurement, and the uncertainty measurement examples show the effectiveness of the new uncertainty measure. Finally, an information fusion method based on the new uncertainty measure is proposed. The validity and rationality of the proposed method are verified by two classification experiments from UCI data sets.

Keywords: Belief structure; Classification; Euclidean distance; Evidence theory; Uncertainty measure.

Grants and funding

The work is supported by the National Key Research and Development Project of China (Grant No. 2020YFB1711900). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.