Research on Attribute Reduction Using Rough Neighborhood Model

Author

He, Ming ; Du, Yong-ping

Author_Institution

Coll. of Comput. Sci., Beijing Univ. of Technol., Beijing, China

Volume

1

fYear

2008

fDate

19-19 Dec. 2008

Firstpage

268

Lastpage

270

Abstract

Rough set theory is an efficient information processing tool used in the discovery of data dependencies. It evaluates the importance of attributes, discovers the patterns of data, reduces all redundant objects and attributes, and seeks the minimum subset of attributes. This paper presents a method for attribute reduction on combination of rough set and neighborhood systems. Neighborhood decision system is investigated by considering relation between two ways and introducing two neighborhood approximation operators. Illustrative results for some databases in UCI repository of machine learning databases provided good results.

Keywords

data mining; learning (artificial intelligence); rough set theory; attribute reduction; data dependencies discovery; information processing tool; machine learning databases; neighborhood approximation operator; neighborhood decision system; rough neighborhood model; rough set theory; Computer science; Databases; Educational institutions; Information management; Information systems; Logic; Machine learning; Rough sets; Seminars; Set theory; attribute reduction; neighborhood approximation space; neighborhood systems; rough set theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Business and Information Management, 2008. ISBIM '08. International Seminar on

Conference_Location

Wuhan

Print_ISBN

978-0-7695-3560-9

Type

conf

DOI

10.1109/ISBIM.2008.13

Filename

5117480