Supervised information granulation strategy for attribute reduction-Reference-Cited by-同舟云学术

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Supervised information granulation strategy for attribute reduction

Published:2020-03-07 Issue:9 Volume:11 Page:2149-2163
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Liu Keyu,Yang Xibei,Yu Hualong,Fujita Hamido,Chen Xiangjian,Liu Dun

Funder

Postgraduate Research & Practice Innovation Program of Jiangsu Province

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

http://link.springer.com/content/pdf/10.1007/s13042-020-01107-5.pdf

Reference63 articles.

1. Chen DG, Li WL, Zhang X, Kwong S (2014) Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets. Int J Approx Reason 55:908–923

2. Chen DG, Yang YY (2014) Attribute reduction for heterogeneous data based on the combination of classical and fuzzy rough set models. IEEE Trans Fuzzy Syst 22:1325–1334

3. Chen DG, Yang YY, Dong Z (2016) An incremental algorithm for attribute reduction with variable precision rough sets. Appl Soft Comput 45:129–149

4. Dai JH, Hu H, Wu WZ, Qian YH, Huang DB (2018) Maximal-discernibility-pair-based approach to attribute reduction in fuzzy rough sets. IEEE Trans Fuzzy Syst 26:2174–2187

5. Dai JH, Hu QH, Hu H, Huang DB (2018) Neighbor inconsistent pair selection for attribute reduction by rough set approach. IEEE Trans Fuzzy Syst 26:937–950

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