为了获得高效率和更简洁的知识 ,一些启发式算法被提出用于基于扩张矩阵理论的示例学习研究 .该文基于粗集理论研究扩张矩阵的示例学习问题 ,并应用遗传算法获取示例学习中的最优概念 .实验结果表明该方法是有效的 .提出了粗集理论下的几个新概念 ,如 :必要选择子 ,核选择子集 ,约简选择子集和所产生复合的评价指标 In order to obtain high efficiency and concise knowledge, some heuristic algorithms have been proposed for example learning based on expansion matrix theory.This paper studies the example learning problems of expansion matrix based on rough set theory, and uses genetic algorithm to obtain the example learning The results show that this method is effective. Several new concepts of rough set theory are proposed, such as the necessary choice of child, the choice of nuclear, the choice of reduced choice and the resulting composite evaluation index