Abstract: The attribute reduction algorithm based on the core of distinct matrix always has poor efficiency in both time and space.A novel heuristic algorithm is proposed for attribute reduction based on rough set theory.Distinct matrix is not constructed and stored directly and a better core set can be obtained even if the core element is not existent.Obtaining the matrix element and core element are carried out simultaneously and the frequency of attribute set is considered in this method.Then the method is applied to medical diagnostic decision-making.Experiment results show that by using the reduced attribute set，it not only can decrease the computational complexity but also can keep high decision accuracy.The algorithm can find a good attribute subset.

Key words: rough set theory, attribute reduction, heuristic algorithm, medical diagnosis

摘要： 针对基于可辨识矩阵核求取属性约简存在的空间与时间都不理想的问题，提出一种新的基于粗糙集的属性约简启发式算法。该方法不直接构造及存储可辨识矩阵，而且在核不存在的情况下，也能取得较好的起点核心集，将获取矩阵元素及得到核心元素同步进行，并加入了对属性集频率的综合考虑。同时，将此方法应用于医疗诊断决策，并对属性约简前后的决策性能进行了分析。实验结果表明，利用约简后的属性集，计算复杂性降低，同时保持高的决策准确率，算法是有效的。

关键词: 粗糙集理论, 属性约简, 启发式算法, 医疗诊断