基于强化正域的属性约简方法

Attribute reduction method based on enhanced positive region

通过对多种约简方法进行比较,为了得到更好的结果,在传统基于属性依赖度的约简方法基础上,定义更精确的强化正域概念。通过对边界域的精确划分,得出各条件属性对决策属性的强化依赖度,并用自顶向下的启发式搜索算法得到约简结果。采用UCI标准数据集对基于强化正域约简方法 REPR进行测试,约简数据后构建的决策树规模小,分类精度高。实验结果表明,相比于经典方法,REPR能更有效地对决策表进行属性约简。

Through a variety of reduction methods were compared, this paper proposed a more precise definition of the positive region based on the traditional attribute dependence in order to get better results. By dividing the boundary region accurately, the dependence of conditional attributes on decision attributes were enhanced, and obtained the results by using the top-down heuristic search algorithm. It used UCI standard data sets to test reduction method based on enhanced positive region REPR. And REPR can reduce the size of the decision tree and improve the classification accuracy, which shows that it can be more efficient for attribute reduction on decision tables than the other classical methods.