不完备信息系统中改进的LEM2算法  

Improved LEM2 Algorithm for Incomplete Information System

作　　者：徐怡[1] 李龙澍[1,2] 李学俊[1,2] 

机构地区：[1]安徽大学计算智能与信号处理教育部重点实验室,安徽合肥230039 [2]安徽大学计算机科学与技术学院,安徽合肥230039

出　　处：《华南理工大学学报（自然科学版）》2010年第11期104-109,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：国家自然科学基金资助项目(60273043);安徽省自然科学基金资助项目(090412054);安徽大学人才科研启动基金资助项目(02303113);安徽省科技攻关计划重大科技专项项目(08010201002)

摘　　要：针对LEM2(Learning from Examples Module,Version 2)算法处理不完备信息系统的局限性,从规则提取的质量和效率两个方面对其进行改进,提出改进的LEM2规则提取算法.基于集对理论,引入集对势容差关系和基于集对势容差关系的扩充粗糙集模型,将该模型和LEM2算法相结合,提高规则提取的质量;定义冗余的属性-值对集合,在规则提取过程中,从候选属性-值对集中直接删除冗余的属性-值对,避免反向消除步骤,加快算法的收敛速度,提高规则提取的效率.最后通过仿真实验,证明了改进LEM2算法用于不完备信息系统规则提取的有效性.In order to exceed the limitations of LEM2（Learning from Examples Module,Version 2） algorithm in processing incomplete information system,an improved LEM2 rule-induction algorithm is proposed,with the qua-lity and efficiency of rule induction being both improved.Then,based on the set pair theory,the set pair situation tolerance relation and the generalized rough set model based on the tolerance relation are introduced in the incomplete information system,and the model is combined with the LEM2 algorithm to improve the quality of rule induction.Moreover,the redundant attribute-value pair set is defined,by deleting which from the candidate attribute-value pair sets during the rule induction,the converse elimination action is successfully avoided,the convergence is accelerated and the efficiency of rule induction is improved.Finally,some simulation experiments are conducted.The results verify the effectiveness of the improved LEM2 algorithm in the rule induction of incomplete information system.

关 键 词：不完备信息 粗糙集 LEM2算法 集对势容差关系 相似关系 容差关系 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]