Abstract: This paper analyzes the principles of Pignistic probability transforms, and introduces three new Pignistic probability transform algorithms for decision making. The experiment results show that the transform algorithms can get best performance evaluation. They assign smaller values of probabilities for smaller values of beliefs while assign higher probability values for higher values of beliefs which lead to better decision making. The researches of Pignistic probability transforms are applicable for other belief-based uncertainty theories.

摘要： 对Pignistic概率转换方法进行了分析,并提出了几种新的基于Pignistic概率的转换方法。实验表明,该文提出的转换方法可获得更好的性能评价,能有效利用现有信息扩大各命题之间的信度差异,在不提高决策风险的前提下,使满足决策阈值的命题数降至最少,更有利于系统的最终决策。

关键词: 信息融合, 决策规则, Pignistic概率

CLC Number: 

• O21