一种新的决策粗糙集启发式属性约简算法

属性约简是粗糙集理论中最重要的研究内容之一。在决策粗糙集中,学者提出了多种属性约简的定义,其中包括保持所有对象正决策不变的约简定义。针对该约简定义,为了高效地获取约简集,设计了一种启发式函数 ——决策重要度,这种启发式函数根据每个属性正决策对象集合的大小来定义其重要性,正决策对象集合越大表示重要性越高,由此构造了基于决策重要度的启发式属性约简算法。该算法的优点是通过对属性决策重要度的排序,确定了一个搜索方向,避免了属性的组合计算,减少了计算量,能够找出一个较小的约简集。实验结果表明,该算法是有效的,能够得到较好的约简效果。

New Heuristic Algorithm for Attribute Reduction in Decision-theoretic Rough Set

Attribute reduction is one of the most important research contents in rough set theory.Scholars have proposed various definitions for attribute reduction in decision-theoretic rough set,including the definition of keep the positive decisions of all objects unchanged.Directing at the positive decision definition,in order to efficiently obtain the reduction set,designed a heuristic function is designed,that is important degree of decision-making.This heuristic function defines the decision important degree of every attribute according to the size of positive decision objects set.The bigger the size of positive decision objects set,the greater the improtance,thus constructs heuristic attribute reduction algorithm based on the decision important degree.The advantage of this algorithm is that it determines the search direction according to the sorting of attribute decision important degree,avoids the calculation of attribute combination,and can reduce the amount of calculation and find out a smaller reduction set.The experimental results show that the algorithm is effective and can obtain a good reduction effect.