Abstract
Attribute reduction is one of the key issues in rough set theory. Many heuristic reduction strategies such as forward heuristic reduction, backward heuristic reduction and for-backward heuristic reduction have been proposed to obtain a subset of attributes which has the same discernibility as the original attribute set. However, some methods are usually computationally time consuming for large data sets. Therefore, this paper focuses on solving the attribute reduction efficiency in the decision system. We first introduce the quotient of approximation, positive region and conflict region, and then research the heuristic reduction algorithm based on conflict region. Sequentially, we put forward to a mechanism of bidirectional heuristic attribute reduction based on conflict region quotient and design a bidirectional heuristic attribute reduction algorithm. Finally, the experimental results with UCI data sets show that the proposed reduction algorithm is an effective technique to deal with large high-dimensional data sets.
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Acknowledgments
Research on this work is partially supported by the grants from the National Science Foundation of China (No. 5130711), by the funds from Anhui Provincial Natural Science Foundation (No. 1308085QF114), by the funds from Anhui Education Natural Science Foundation (Nos. KJ2013A015, KJ2012A212), and by the fund from Key Laboratory of Computation Intelligence and Signal Processing of Education Ministry Foundation.
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Ge, H., Li, L., Xu, Y. et al. Bidirectional heuristic attribute reduction based on conflict region. Soft Comput 19, 1973–1986 (2015). https://doi.org/10.1007/s00500-014-1382-x
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DOI: https://doi.org/10.1007/s00500-014-1382-x