Attribute Reduction of Neighborhood Rough Set Based on Discernment

© 2024 by the Authors. Licensee Whioce Publishing, Singapore. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )

Download PDF

Cite

XML

HTML

Abstract

For neighborhood rough set attribute reduction algorithms based on dependency degree, a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed. The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes, allowing for a more accurate measurement of the importance degrees of attributes. Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

References

Pawlak Z, 1982, Rough sets. International Journal of Computer & Information Science, 11: 341–356. https://doi.org/10.1007/BF01001956

Deng Z-X, Zheng Z-L, Deng D-Y, 2021, F-Neighborhood Rough Sets and Its Reduction. Acta Automatica Sinica, 47(3): 695?705. https://doi.org/10.16383/j.aas.c180556

Ji J-Z, Song X-N, Yang C-C, 2020, Feature Reduction of Neighborhood Rough Set Based on Fish Swarm Algorithm in Brain Functional Connectivity. Journal of Zhejiang University (Engineering Science), 54(11): 2247–2257. https://doi.org/10.3785/j.issn.1008-973X.2020.11.020

Chen T, Liu J, Zhu F, et al., 2018, A Novel Multi-Radius Neighborhood Rough Set Weighted Feature Extraction Method for Remote Sensing Image Classification. Geomatics and Information Science of Wuhan University, 43(2): 311–317. https://doi.org/10.13203/j.whugis20150290

Wu Y, Zhao R, Jin W, 2019, Fault Feature Extraction of Rotating Machinery Based on EWT and a Weighted Multi Neighborhood Rough Set. Journal of Vibration and Shock, 38(24): 235–242.

Wang H, Wang S, Yao J, et al., 2019, Tag Identification Rate Prediction Based on Neighborhood Rough Set and Support Vector Machine. Computer Integrated Manufacturing Systems, 25(12): 3170–3180.

Zhang W, Wu W, Liang J, et al., 2001, Rough Set Theory and Method. Science Press, Beijing.

Hu Q-H, Yu D-R, Xie Z-X, 2008, Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation. Journal of Software, 19(3): 640–649.

Liu X, Wang X-L, 2021, Value Segmentation of Airline Customers Based on K-means and Neighborhood Rough Set. Operations Research and Management Science, 30(3): 104–111.

Chen Y-M, Zhu Q-X, Zeng Z-Q, et al., 2018, Gene Selection Method Based on Neighborhood Rough Sets and Fish Swarm Intelligence. Journal of University of Electronic Science and Technology of China, 47(1): 99–104.

Yong L, Huang W, Jiang Y, et al., 2014, Quick Attribute Reduct Algorithm for Neighborhood Rough Set Model. Information Sciences, 271: 65–81. https://doi.org/10.1016/j.ins.2014.02.093

Previous article in this issue

Next article in this issue