基于属性排斥矩阵的约简方法

针对启发式算法难以获得最小约简的问题,研究最小约简约束下属性之间的排斥特性,提出了针对部分最小约简必要条件的属性排斥矩阵.在此基础上,分别结合典型加法类和减法类启发式约简算法提出两种改进的基于属性排斥矩阵的启发式属性约简算法,采用UCI(加州大学欧义分校)机器学习数据集所进行的测试结果表明,属性排斥矩阵能够全面提高启发式属性约简算法的性能,有利于获得最小约简.

Reduction Method Based on Attribute Repulsion Matrix

In order to calculate a minimal reduct based on a heuristic reduction algorithm, the character of attribute repulsion related to the minimal reducts is analyzed and an attribute repulsion matrix is proposed. On this basis, by combining some classical addition and deletion methods, two improved heuristic reduction algorithms based on the attribute repulsion matrix are presented. The experimental results on some UCI(UC Irvine) machine learning data sets show that the proposed attribute repulsion matrix can completely improve the quality of reduct and is helpful for a heuristic algorithm to calculate the minimal reduct.