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Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007)
An Improved Attribute Reduction Algorithm Based on Rough Set
Haier International Training Center, Qingdao, China
July 30-August 01
ISBN: 0-7695-2909-7
ASCII Text
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Haijun Wang, Shaoliang Wei, Yimin Chen, "An Improved Attribute Reduction Algorithm Based on Rough Set," Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on, vol. 3, pp. 1007-1010, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007.
 
 
BibTex
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@article{ 10.1109/SNPD.2007.181,
author = {Haijun Wang and Shaoliang Wei and Yimin Chen},
title = {An Improved Attribute Reduction Algorithm Based on Rough Set},
journal ={Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on},
volume = {3},
year = {2007},
isbn = {0-7695-2909-7},
pages = {1007-1010},
doi = {http://doi.ieeecomputersociety.org/10.1109/SNPD.2007.181},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
TI - An Improved Attribute Reduction Algorithm Based on Rough Set
SN - 0-7695-2909-7
SP1007
EP1010
A1 - Haijun Wang,
A1 - Shaoliang Wei,
A1 - Yimin Chen,
PY - 2007
KW - null
VL - 3
JA - Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, ACIS International Conference on
ER -
 
Haijun Wang, Shanghai University, China
Shaoliang Wei, Shanghai University, China
Yimin Chen, Shanghai University, China
An improved heuristic attribute reduction algorithm based on the attribute frequency is presented. After analyzing many other attribute reduction algorithms, we utilize the discernibility matrix and the appeared attribute frequencies to determine each attribute?s significance, based on the principle of maximum attribute frequency,we achieved the reduction of the information system. An illustrative example demonstrate the algorithm?s effectiveness and validity.
Citation:
Haijun Wang, Shaoliang Wei, Yimin Chen, "An Improved Attribute Reduction Algorithm Based on Rough Set," snpd, vol. 3, pp.1007-1010, Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), 2007
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