基于熵和变精度粗糙集的规则不确定性量度

由已知数据中产生的粗糙决策规则往往具有不确定性 ,需要适当的不确定性量度。借鉴变精度粗糙集理论的思想 ,采用基于信息熵的方法构造了两个新的粗糙决策规则不确定性量度函数。它们不仅可以兼顾由划分的粒度引起的规则不确定性的两个方面 ,即不一致性和随机性 ,还考虑了数据中的噪声对规则一致性的影响。因此 ,它们对一类“几乎一致性规则”具有一定的保护作用。通过举例分析 ,说明它们更适于评价从有噪声数据中提取的粗糙决策规则。

Uncertainty measures of rules based on entropy and variable precision rough set

The evaluation of the uncertainty of rough decision rules retrieved from a given data set needs proper uncertainty measures. Two new information entropy-based uncertainty measures are presented based on variable precision rough set theory. They deal with the two aspects of uncertainty of rules coming from the granularity of the partition, namely inconsistency and randomness. Also they consider the influence of the noise in the data upon the consistency of the rules. As a result, they can propose some “nearly consistent rules”. A simple example is used to illustrate their fitness to evaluate rough decision rules retrieved from noisy data.