摘要
传统的属性约简算法效率低下,容易陷入局部极小值,不适用于大型知识库.文中提出一种基于粗糙集理论的遗传属性约简方法,在传统的属性约简方法基础上对适应度函数、交叉和变异的概率、变异方式和种群修复方式进行了改进.在正域区分对象集的研究基础上,用启发信息设计了一种快速的属性约简算法,并利用Matlab工具进行仿真,将仿真结果与前人研究结果作对比.实验表明此算法优于前人的算法,能够快速高效地对大型知识系统求其约简.
Traditional attribute reduction algorithm efficiency is low. It is easy to fall into local minimum value and shall not be applied to the large decision table. This paper proposes a genetic attribute reduction method based on rough set theory. Compared with traditional attribute reduction methods,it improves the fitness function the crossover probability,the mutation probability and the mutation methods. It takes advantage of heuristic in-formation in design a new efficient genetic algorithm of attribute reduction based on rough set. It makes use of Matlab tools to the simulation and compares the simulation results with predecessors＆#39; research results. The emu-late example and experiment results show that the algorithm could compute the attribute reduction of the decision table quickly and efficiently,especially in tackling a large decision table.
出处
《江苏科技大学学报（自然科学版）》
CAS
2014年第3期271-276,共6页
Journal of Jiangsu University of Science and Technology:Natural Science Edition
关键词
粗糙集
属性约简
属性分类能力
遗传算法
变异方式
rough set
attribute reduction
attribute classification ability
genetic algorithm
mutation methods
