基于边界域条件熵的最优尺度约简

Optimal scale reduction based on boundary domain conditional entropy

由于现实世界中属性具有多层次多尺度,因此多尺度决策表的概念被提出.目前对多尺度决策表的研究大多集中在最优尺度组合上,但通过最优尺度组合得到的并不是一个真正的约简集,仍需再次进行属性约简,因此可能会导致求约简的时间较长.为此考虑利用边界域条件熵直接求最优尺度约简.首先,引入多尺度决策表中最优尺度约简的定义,给出多种最优尺度约简的定义,探讨在协调和不协调两种背景下几种最优尺度约简之间的关系.其次,给出多尺度决策表中边界域条件熵的定义,讨论边界域条件熵的若干性质以及与约简的关系.最后,给出基于边界域条件熵的最优尺度约简算法,并用实验验证该方法的有效性.

The concept of multi-scale decision tables is proposed because the attributes are with the multi-level and multi-scale in the real world.At present,most researches on multi-scale decision tables focus on optimal scale combination,but the optimal scale combination is not a real reduction set.It still needs to be reduced again.So it might lead to a higher reduction time.Therefore,the boundary domain conditional entropy is used to obtain the optimal scale reduction directly.Firstly,the definition of optimal scale reduction in multi-scale decision tables is introduced,and the definition of multiple optimal scale reductions is given.The relationship between several optimal scale reductions under the background of consistent and inconsistent is discussed.Secondly,the concept of boundary domain conditional entropy is introduced into multi-scale decision tables,and some properties of boundary domain conditional entropy and its relationship with reductions are discussed.Finally,an optimal scale reduction algorithm based on boundary domain conditional entropy is given and the effectiveness of the method is validated by experiments.