Extract Rules by Using Rough Set and Knowledge-Based NN

作     者：王士同 E.Scott 等 

作者机构：DepartmentofComputerEastChinaShipbuildingInstituteZhenjiang212003PR.China DepartmentofComputerRo 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：1998年第13卷第3期

页      面：279-284页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Rough set theory ,knowledge-based NN (neural network) knowledge discovery machine learning 

摘      要：In this paper, rough set theory is used to extract roughly-correct inference rules from information systems. Based on this idea, the learning algorithm ERCR is presented. In order to refine the learned roughly-correct inference rules, the knowledge-based neural network is used. The method presented here sufficiently combines the advanages of rough set theory and neural network.