A Time-Reduction Strategy to Feature Selection in Rough Set Theory

Authors:
Hongxing Chen;Yuhua Qian;Jiye Liang;Wei Wei;Feng Wang
Affiliations:
Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, Taiyuan, Shanxi, China 030006;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, Taiyuan, Shanxi, China 030006;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, Taiyuan, Shanxi, China 030006;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, Taiyuan, Shanxi, China 030006;Key Laboratory of Computational Intelligence and Chinese Information Processing, of Ministry of Education, Taiyuan, Shanxi, China 030006
Venue:
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
Year:
2009

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Abstract

In rough set theory, the problem of feature selection aims to retain the discriminatory power of original features. Many feature selection algorithms have been proposed, however, quite often, these methods are computationally time-consuming. To overcome this shortcoming, we introduce a time-reduction strategy, which can be used to accelerate a heuristic process of feature selection. Based on the proposed strategy, a modified feature selection algorithm is designed. Experiments show that this modified algorithm outperforms its original counterpart. It is worth noting that the performance of the modified algorithm becomes more visible when dealing with larger data sets.