不完备决策信息系统的混合条件熵与多属性决策

Hybrid conditional entropy and multi-attribute decision making of incomplete decision information system

不完备决策信息系统的多属性决策具有应用意义,其主要依托不确定性度量.针对不完备决策信息系统,现有条件熵及相关决策排序方法具有信息单一缺点,对此提出混合条件熵与对应决策排序方法.首先,层次分解条件熵并引入依赖度,构建直接混合与层次混合两种新型条件熵,得到粒化单调性与度量值大小等性质.然后,由两种混合条件熵设计两种多属性决策排序算法,得到对比五种排序方法的有效性与改进性.最后,实施实际数据集的决策排序实验,证实新建排序方法能够取得条件熵排序方法的一致效果.混合条件熵分层融合了信息表示与代数表示,改进现有条件熵进而更全面表征系统不确定性,其合理排序有利于不完备决策信息系统的多属性决策应用.

Multi-attribute decision making of incomplete decision information system(IDIS)has application signification,and it mainly relies on uncertainty measurement.Aiming at IDIS,the conditional entropy and related decision sorting method have information singleness,so the hybrid conditional entropy and corresponding decision sorting methods are proposed.At first,the conditional entropy is hierarchically decomposed,while the dependency degree is introduced;two new types of conditional entropy with direct and hierarchical fusion are constructed,and their properties of granulation monotonicity and measurement sizes are acquired.Then,two multi-attribute decision sorting algorithms are designed by two types of hybrid conditional entropy,and they exhibit effectiveness and improvements in contrast to five sorting ways.Finally,decision ranking experiments are implemented by real data sets,and the new sorting strategies are verified to achieve consistent effects for the existing algorithm based on conditional entropy.The hybrid conditional entropy hierarchically combines the information and algebra representations,and it improves the current conditional entropy to comprehensively characterize the system uncertainty,so its rational sorting profits multi-attribute decision making applications in IDIS.