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Proceedings Paper

A fast algorithm for attribute reduction based on Trie tree and rough set theory
Author(s): Feng Hu; Xiao-yan Wang; Chuan-jiang Luo
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Paper Abstract

Attribute reduction is an important issue in rough set theory. Many efficient algorithms have been proposed, however, few of them can process huge data sets quickly. In this paper, combining the Trie tree, the algorithms for computing positive region of decision table are proposed. After that, a new algorithm for attribute reduction based on Trie tree is developed, which can be used to process the attribute reduction of large data sets quickly. Experiment results show its high efficiency.

Paper Details

Date Published: 13 March 2013
PDF: 8 pages
Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87841E (13 March 2013); doi: 10.1117/12.2014029
Show Author Affiliations
Feng Hu, Chongqing Univ. of Posts and Telecommunications (China)
Southwest Jiaotong Univ. (China)
Xiao-yan Wang, Chongqing Univ. of Posts and Telecommunications (China)
Chuan-jiang Luo, Chongqing Univ. of Posts and Telecommunications (China)


Published in SPIE Proceedings Vol. 8784:
Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies
Yulin Wang; Liansheng Tan; Jianhong Zhou, Editor(s)

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