基于粒度决策熵的属性约简

近年来，人们越来越关注粗糙集中的属性约简算法，尤其是启发式的约简算法。为了度量属性重要度，人们把各种不同的信息熵模型应用到粗糙集中，同时在信息熵这一理论的基础上得出了许多约简算法，用来解决粗糙集中属性约简的问题。然而，现有的基于信息熵的方法还存在一系列问题。针对这些问题，本文首先将知识粒度与相对决策熵这2个概念结合在一起，从而引入一种新的信息熵模型--粒度决策熵；然后，利用粒度决策熵来度量属性的重要性，并由此得出新的约简算法--ARGDE约简算法；最后，用不同的UCI数据集来做实验，通过与已有的约简算法比较，该算法能够得到更好的实验结果。

Attribute Reduction Based on Granularity Decision Entropy

In recent years, more and more attention has been paid to the attribute reduction algorithm of rough set, especially the heuristic reduction algorithm. In order to measure the attribute importance, people used different kinds of information entropy model in rough set, and obtained many reduction algorithms on the basis of the theory of information entropy to solve the problem of attribute reduction of rough set. However, there are a number of problems in the existing information entropy methods. To solve these problems, this paper firstly combines the knowledge granularity and relative decision entropy, and introduces a new information entropy model-the granularity decision entropy. Then, using the granularity decision entropy to measure the importance of attributes, the new reduction algorithm-ARGDE reduction algorithm is obtained. Finally, different UCI data sets are used to perform the experiment, and the algorithm can get better results by comparing with the existing reduction algorithms.