基于证据熵对不确定性度量的决策表约简

Decision Table Reduction Based on Evidence Entropy for Uncertainty Measures

知识约简是粗糙集理论的核心内容之一,产生的粗糙决策规则往往具有一定的不确定性。在变精度粗糙集的基础上,本文构造了符合证据理论框架的一组焦元,利用基本概率分配函数计算了证据的总体信息熵,度量了决策表的不确定性;以该度量作为启发信息,给出了决策表的启发式知识约简算法。计算实例表明了本文方法的有效性。

Knowledge reduction is one of the important topics in the research on rough set theory, and rough decision rutles are inevitably provided with uncertainty. In this paper, a family of focal sets is constructed within the framework of evidence theory on the basis of variable precision rough set theory. Accordingly, the function of basic probability assignment is defined, and then the total information entropy is calculated for evidence theory, namely the evidence entropy. Uncertainty measure for the decision table is determined by that entropy. Based on the measure, the heuristic algorithm is proposed for decision table reduction. Finally, the experimental results show the validity of the methodology.