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2018影响因子

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## 留言板

F-邻域粗糙集及其约简

 引用本文: 邓志轩, 郑忠龙, 邓大勇. F-邻域粗糙集及其约简. 自动化学报, 2021, 47(3): 695-705
Deng Zhi-Xuan, Zheng Zhong-Long, Deng Da-Yong. F-neighborhood rough sets and its reduction. Acta Automatica Sinica, 2021, 47(3): 695-705 doi: 10.16383/j.aas.c180556
 Citation: Deng Zhi-Xuan, Zheng Zhong-Long, Deng Da-Yong. F-neighborhood rough sets and its reduction. Acta Automatica Sinica, 2021, 47(3): 695-705

• 本文责任编委  张敏灵

## F-neighborhood Rough Sets and Its Reduction

Funds:

National Natural Science Foundation of China 61672467

###### Corresponding author:ZHENG Zhong-Long  Professor at the College of Mathematics and Computer Science, Zhejiang Normal University. He received his Ph. D. degree from Shanghai Jiao Tong University in 2005. His research interest covers machine learning and pattern recognition. Corresponding author of this paper
• Recommended by Associate Editor ZHANG Min-Ling
• 摘要: 邻域粗糙集可以直接处理数值型数据, F- 粗糙集是第一个动态粗糙集模型. 针对动态变化的数值型数据, 结合邻域粗糙集和F- 粗糙集的优势, 提出了F- 邻域粗糙集和F- 邻域并行约简. 首先, 定义了F- 邻域粗糙集上下近似、边界区域; 其次, 在F- 邻域粗糙集中提出了F- 属性依赖度和属性重要度矩阵; 根据F- 属性依赖度和属性重要度矩阵分别提出了属性约简算法, 证明了两种约简方法的约简结果等价; 最后, 比对实验在UCI数据集、真实数据集和MATLAB生成数据集上完成, 实验结果显示, 与相关算法比较, F- 邻域粗糙集可以获得更好的分类准确率. 为粗糙集在大数据方面的应用增加了一种新方法.
Recommended by Associate Editor ZHANG Min-Ling
1)  本文责任编委  张敏灵
• 图  1  概念XFIS中的上近似、下近似、边界区域、负区域

Fig.  1  Concept X in the FIS upper approximation, lower approximation, boundary region, and negative region

图  2  在各个数据集中算法的分类准确率

Fig.  2  Classification accuracy of algorithms in each dataset

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##### 出版历程
• 收稿日期:  2018-08-20
• 录用日期:  2019-01-02
• 刊出日期:  2021-04-02

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