IMI-BAS BAS
 

BulDML at Institute of Mathematics and Informatics >
IMI >
IMI Periodicals >
Serdica Journal of Computing >
2013 >
Volume 7 Number 4 >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/2420

Title: Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems
Authors: Dinh, Vu Van
Giang, Nguyen Long
Keywords: Rough Set
Tolerance-Based Rough Set
Decision System
Incomplete Decision System
Attribute Reduction
Reduct
Issue Date: 2013
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Serdica Journal of Computing, Vol. 7, No 4, (2013), 375p-388p
Abstract: A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.
URI: http://hdl.handle.net/10525/2420
ISSN: 1312-6555
Appears in Collections:Volume 7 Number 4

Files in This Item:

File Description SizeFormat
sjc-vol7-num4-2013-p375-p388.pdf178.18 kBAdobe PDFView/Open

 



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0!   Creative Commons License DSpace Software Copyright © 2002-2009  The DSpace Foundation - Feedback