Generalized binary discernibility matrix for attribute reduction in incomplete information systems  

作　　者：Ma Fumin Zhang Tengfei 

机构地区：[1]College of Information Engineering, Nanjing University of Finance and Economics [2]College of Automation, Nanjing University of Posts and Telecommunications

出　　处：《The Journal of China Universities of Posts and Telecommunications》2017年第4期57-68,75,共13页中国邮电高校学报（英文版）

基　　金：supported by the National Natural Science Foundation of China (61403184, 61105082);the ‘1311 Talent Plan’ of Nanjing University of Posts and Telecommunications (NY2013);the ‘Qinglan’ Project of Jiangsu Province (QL2016);the Natural Science Foundation of Nanjing University of Posts and Telecommunications (215149);the Priority Academic Program Development of Jiangsu Higher Education Institutions, (PAPD);the Major Program of the Natural Science Foundation of Jiangsu Province Education Commission (17KJA120001)

摘　　要：To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.To extract and express the knowledge hidden in information systems, discernibility matrix and its extensions were introduced and applied successfully in many real life applications. Binary discernibility matrix, as a representative approach, has many interesting superior properties and has been rapidly developed to find intuitive and easy to understand knowledge. However, at present, the binary discernibility matrix is mainly adopted in the complete information system. It is a challenging topic how to achieve the attribute reduction by using binary discernibility matrix in incomplete information system. A form of generalized binary discernibility matrix is further developed for a number of representative extended rough set models that deal with incomplete information systems. Some useful properties and criteria are introduced for judging the attribute core and attribute relative reduction. Thereafter, a new algorithm is formulated which supports attribute core and attribute relative reduction based on the generalized binary discernibility matrix. This algorithm is not only suitable for consistent information systems but also inconsistent information systems. The feasibility of the proposed methods was demonstrated by worked examples and experimental analysis.

关 键 词：rough set generalized binary discernibility matrix attribute relative reduction incomplete information system 

分 类 号：TN91[电子电信—通信与信息系统]