基于广义决策函数的改进LEM2规则提取算法  

Improved LEM2 Rule Extraction Algorithm Based on Generalized Decision Function

作　　者：纪霞[1,2] 李龙澍[1,2] 徐怡[1,2] 

机构地区：[1]安徽大学计算智能与信号处理重点实验室,安徽合肥230039 [2]安徽大学计算机学科学与技术院,安徽合肥230601

出　　处：《华南理工大学学报（自然科学版）》2014年第5期143-148,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：安徽省自然科学基金资助项目(1308085QF114);安徽省高等学校自然科学基金资助项目(KJ2012Z020;KJ20133A015);安徽大学博士科研启动基金资助项目(33190081)

摘　　要：针对当前LEM2系列算法提取规则的效率和质量不高的问题,提出了基于广义决策函数的改进LEM2算法(GLD-LEM2).该算法根据广义决策函数来计算候选属性-值对集T(G),通过删除冗余属性-值对来逐步缩小T(G)的规模,以提高规则提取的效率;同时,根据广义决策函数相交最小原则来选择属性-值对,优先提取最简规则,以提高获取规则的质量.实验结果表明,对于完备或不完备的决策表规则,GLD-LEM2算法均能有效地提高规则提取的效率和质量.In order to improve the efficiency and quality of rule extraction in LEM2 series algorithms, an improved LEM2 algorithm on the basis of generalized decision function, namely GDF-LEM2, is proposed. In this algorithm, candidate attribute-value pair set T（G） is calculated according to generalized decision function and is downsized by deleting newly-defined redundancy attribute-value pair sets, and thus the efficiency of rule extraction is improved. Moreover, the choice of attribute-value pair sets is guided with the minimum intersection of generalized decision function strategy, which makes the extracted rule more laconic and thus improves the quality of rule extraction. Ex- perimental results show that GDF-LEM2 algorithm effectively improves the efficiency and quality of rule extraction from complete or incomplete decision systems.

关 键 词：粗糙集 规则提取 LEM2算法 广义决策函数 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]