Partial attribute reduction approaches to relation systems and their applications

• Published in: Knowledge-based systems Vol. 139; pp. 101 - 107
• Main Authors: Liu, Guilong, Hua, Zheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.01.2018; Elsevier Science Ltd
• Subjects: Algorithms; Approximation; Data analysis; Discernibility matrix; Information systems; Lower approximation reduction; Mathematical analysis; Reduction; Relation system; Relational decision system; Rough set models; Set theory; Upper approximation reduction; X-lower approximation reduction; Relation system; Lower approximation reduction; Upper approximation reduction; X-lower approximation reduction; Relational decision system; Discernibility matrix
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2017.10.014

•Propose the concept of the X-lower reduction for relation systems.•Study the lower approximation reduction for relation decision systems.•Study the upper approximation reduction for relation decision systems.•Positive region reduction for decision tables is a special case of our reductions. Attribute reduction has long been an active subject of research in rough set theory, and constitutes an important step in data analysis. A relation system is an extension of a typical information system. This paper proposes the concepts of X-lower and -upper approximation reductions, and develops corresponding reduction algorithms for general relation systems. By using these types of reduction, we derive lower and upper approximation reductions for relation decision systems. As a special case, we obtain a reduction algorithm for the positive region for decision tables. Finally, we provide two examples from the University of California–Irvine (UCI) datasets to verify our theoretical results.