分类算法中基于差别矩阵的属性约简方法

差别矩阵中会出现大量的重复元素占用大量内存,当数据太稠密时,构成的差别矩阵太大不容易操作且计算代价较高。本文提出了一种基于简化差别矩阵的属性约简算法（SDMAR）,在属性约简之前,通过计算属性相似度,对属性进行了合并操作,得到简化决策表。根据简化决策表构造差别矩阵,计算差别矩阵中出现次数最多的属性并删除包含该属性的元素,当差别矩阵为空时终止操作,以达到对决策表属性约简的目的。通过算法及实例分析得到属性约简过程的时间复杂度有所减小。

Attribute reduction algorithm based on discernibility matrices in classification

Large numbers of duplicate elements contained in discernibility matrix may employ a lot of memory.When the dataset is dense,the time complexity of constructing discernibility matrix is very high.In this paper,an attribute reduction algorithm SDMAR based on simplified discernibility matrix is proposed.Firstly,the attributes are merged by calculating the attributes similarity and the same individuals in universe are deleted before reducing attribute,then a simplified decision table is got.Secondly, simplified discernibility matrix is constructed according to reduction decision table.To achieve the purpose of attribute reduction, the attributes occurred most frequent in discernibility matrix are found and the elements included these attributes are deleted,until discernibility matrix is empty.The analysis of the algorithm and a case shows the time complexity of attribute reduction is low.