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 improvedLEM2 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 bydeleting 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 decisionfunction 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 extractionfrom complete or incomplete decision systems.