Approaches for coarsest granularity based near-optimal reduct computation
Author:
Funder
Council of Scientific and Industrial Research, India
uoh-ioe
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-03571-8.pdf
Reference41 articles.
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3. Xu B, Chen H, Zhu W, Zhu X (2013) Multi-objective cost-sensitive attribute reduction. In: 2013 Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). https://doi.org/10.1109/IFSA-NAFIPS.2013.6608602, IEEE, pp 1377–1381
4. Zhao H, Min F, Zhu W (2011) Test-cost-sensitive attribute reduction based on neighborhood rough set. In: 2011 IEEE International conference on granular computing, IEEE, pp 802–806
5. Inuiguchi M (2017) Attribute importance degrees corresponding to several kinds of attribute reduction in the setting of the classical rough sets. In: Fuzzy Sets, Rough Sets, Multisets and Clustering. https://doi.org/10.1007/978-3-319-47557-8_14, vol 671. Springer, pp 241–255
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Shortest-length and coarsest-granularity constructs vs. reducts: An experimental evaluation;International Journal of Approximate Reasoning;2024-07
2. Attribute Reduction Algorithm for Incomplete Information Systems Based on Intuitive Fuzzy Pairs;IEEE Access;2023
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