基于粗糙集条件信息熵的权重确定方法

权重确定是管理决策和评价的重要环节,现有的权重确定方法基本依赖于专家的先验知识。粗糙集权重确定方法由于其不需要所处理数据集合的先验信息、充分体现数据客观性的特点在管理决策中得到日益广泛应用。但是,原有的粗糙集权重确定方法无法确定冗余属性的权重。本文针对粗糙集的信息表示比代数表示更全面的特点,通过粗糙集条件信息熵属性重要度的分析,提出了新的基于粗糙集条件信息熵的权重确定方法,并分析了其合理性。通过算例证明,新的条件信息熵权重确定方法可以解决原有粗糙集权重确定方法无法解决的问题,从而提高了方法的普适性和可解释性。

A New Method of Ascertaining Attribute Weight Based on Rough Sets Conditional Information Entropy

Attribute weighting is an important approach in management decision and evaluation. The common weighting methods are always relied on the expert's transcendental knowledge. In rough set weighting method,no transcendental knowledge about the disposing data sets is needed,which reflects the objectivity of the data. So this method is more and more applied in management decision,with the shortage that redundant attribute weight could not be ascertained in current studies. Based on the characteristic that the information expression of rough set is more comprehensive than the algebraic expression,attribute importance represented by rough set information entropy is studied deeply in this paper,and a new method of ascertaining attribute weight is put forward based on rough set conditional information entropy,and it's rationality is proved. Finally,a case is used to prove that new weighting method could solve the existing problem and is more applicable and explicable.