粗糙集连续属性离散化通用模型及GASA方法

在对典型的离散化方法分析的基础上,提出一种适用于粗糙集决策表的连续属性离散化处理的通用模型结构;对遗传算法的适应度线性尺度变换作改进,将模拟退火的思想引入遗传算法,提出基于遗传模拟退火算法(GASA)的数据离散化方法,并用UCI机器学习数据库中的Iris和Glass数据集进行验证.实验结果表明,离散化方法通用模型对数据...

Universal model and GASA method for discretization of continuous attribute in rough sets

A universal model framework suitable for dealing with continuous attribute discretization of rough set decision table was presented,based on the analysis of typical discretization method.The genetic simulated annealing algorithm(GASA)-based data discretization method was proposed by modifying the transformation of linear scaling of fitness in GA and introducing the thought of SA into GA.The proposed method was applied to the Iris and Glass data sets in the UCI machine learning repository to verify its valid...