A fast algorithm for attribute reduction based on Trie tree and
rough set theory

Feng Hu; Xiao-yan Wang; Chuan-jiang Luo

doi:10.1117/12.2014029

13 March 2013 A fast algorithm for attribute reduction based on Trie tree and rough set theory

Feng Hu, Xiao-yan Wang, Chuan-jiang Luo

Proceedings Volume 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies; 87841E (2013) https://doi.org/10.1117/12.2014029
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

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.

Citation Download Citation

Feng Hu, Xiao-yan Wang, and Chuan-jiang Luo "A fast algorithm for attribute reduction based on Trie tree and rough set theory", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87841E (13 March 2013); https://doi.org/10.1117/12.2014029

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