Volume 6 Number 6 (Jun. 2011)
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JSW 2011 Vol.6(6): 977-984 ISSN: 1796-217X
doi: 10.4304/jsw.6.6.977-984

Symbolic Representation for Rough Set Attribute Reduction Using Ordered Binary Decision Diagrams

Qianjin Wei1, 2, Gu Tianlong1

1School of Electronic Engineering, XiDian University, Xian, P.R.China
2School of Computer Science, Guilin University of Electronic Technology, Guilin, P.R.China


Abstract—The theory of rough set is the current research focus for knowledge discovery, attribute reduction is one of crucial problem in rough set theory. Most existing attribute reduction algorithms are based on algebra and information representations, discernibility matrix is a common knowledge representation for attribute reduction. As problem solving under different knowledge representations corresponding to different difficulties, by changing the method of knowledge representation, a novel knowledge representation to represent the discernibility matrix using ordered binary decision diagrams (OBDD) is proposed in this paper, the procedures to translate the discernibility matrix model to the conversion OBDD model is presented, experiment is carried to compare the storage space of discernibility matrix with that of OBDD, results show that OBDD model has better storage performance and improve the attribute reduction for those information systems with more objects and attributes, it provide the foundation for seeking new efficient algorithm of attribute reduction.

Index Terms—rough set, attribute reduction, discernibility matrix, ordered binary decision diagrams

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Cite: Qianjin Wei, Gu Tianlong, "Symbolic Representation for Rough Set Attribute Reduction Using Ordered Binary Decision Diagrams," Journal of Software vol. 6, no. 6, pp. 977-984, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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