期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 46, 期 1, 页码 93-108出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2015.2421883
关键词
Decision making; entropy; evidence theory; probabilistic information content (PIC); probability transformation (PT)
资金
- State Key Program for Basic Research of China (973) [2013CB329405]
- National Natural Science Foundation [61104214, 61203222, 61174138]
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61221063]
- Science and Technology Project of Shaanxi Province [2013KJXX-46]
- Specialized Research Fund for the Doctoral Program of Higher Education [20120201120036]
- Fundamental Research Funds for the Central Universities [xjj2012104, xjj2014122]
The transformation of belief function into probability is one of the most important and common ways for decision making under the framework of evidence theory. In this paper, we focus on the evaluation of such probability transformations (PTs), which are crucial for their proper applications and the design of new ones. Shannon entropy or probabilistic information content (PIC) measure is traditionally used in evaluating PTs. The transformation having the lowest entropy or highest PIC is considered as the best one. This standpoint is questioned in this paper by comparing a PT based on uncertainty minimization with other available PTs. It shows experimentally that entropy or PIC is not comprehensive to evaluate a PT. To make a comprehensive evaluation, some new approaches are proposed by the joint use of PIC and the distance of evidence according to the value-and rank-based fusion. A pattern classification application oriented evaluation approach for PTs is also proposed. Some desired properties for PTs are also discussed. Experimental results and related analysis are provided to show the rationality of the new evaluation approaches.
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