摘要
提出了一种基于变精度粗糙集理论的规则挖掘算法 .通过粗糙规则集的不确定性量度 ,应用遗传算法求取相对属性约简 ,然后根据所给阈值导出粗糙规则集 ,并对阈值对规则集的影响进行了事后分析 .由该算法得到的规则既有一定的噪声容忍度又具备较高的准确度和覆盖度 ,从而能充分保证预测和分类的准确性 .实例分析证明 ,该算法是规则挖掘的有效方法 .
A rough rules mining approach based on variable precision rough set theory is proposed in this paper. With uncertain measurement of rough rules set, relative attribute reduction is obtained by applying GA, and then rough rules set is deduced under the threshold values. The effect of threshold values on the rules set is discussed in this paper. The rules obtained by this approach have a certain noise tolerance, more precision and better overlay. So the accuracy of forecasting and classifying are assured. The practical results show that the approach is effective in solving knowledge mining problems.
出处
《信息与控制》
CSCD
北大核心
2004年第1期18-22,30,共6页
Information and Control
基金
国家自然科学基金资助项目 ( 70 1710 5 6)
国家重点科技攻关资助项目 ( 975 62 0 10 7)
关键词
粗糙集
数据挖掘
不确定性
遗传算法
覆盖度
准确度
variable precision rough set
rough rules set
rules mining approach
weak entire accordance
threshold value stable interval
