GBRS: A Unified Granular-Ball Learning Model of Pawlak Rough Set and Neighborhood Rough Set

被引:10
作者
Xia, Shuyin [1 ,2 ]
Wang, Cheng [1 ,2 ]
Wang, Guoyin [1 ,2 ]
Gao, Xinbo [1 ,2 ]
Ding, Weiping [3 ]
Yu, Jianhang [1 ,2 ]
Zhai, Yujia [4 ]
Chen, Zizhong [4 ]
机构
[1] Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Key Lab Cyberspace Big Data Intelligent Secur, Minist Educ, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Key Lab Big Data Intelligent Comp, Chongqing 400065, Peoples R China
[3] Nantong Univ, Sch Informat Sci & Technol, Nantong 226019, Peoples R China
[4] Univ Calif Riverside, Dept Comp Sci & Engn, Riverside, CA 92521 USA
基金
中国国家自然科学基金;
关键词
Rough sets; Feature extraction; Filtering theory; Computational modeling; Filtering algorithms; Classification algorithms; Training; Attribute reduction; feature selection; granular ball; granular computing; neighborhood rough set (NRS); ATTRIBUTE REDUCTION; FEATURE-SELECTION; CLASSIFICATION; ALGORITHM; DISTANCE;
D O I
10.1109/TNNLS.2023.3325199
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Pawlak rough set (PRS) and neighborhood rough set (NRS) are the two most common rough set theoretical models. Although the PRS can use equivalence classes to represent knowledge, it is unable to process continuous data. On the other hand, NRSs, which can process continuous data, rather lose the ability of using equivalence classes to represent knowledge. To remedy this deficit, this article presents a granular-ball rough set (GBRS) based on the granular-ball computing combining the robustness and the adaptability of the granular-ball computing. The GBRS can simultaneously represent both the PRS and the NRS, enabling it not only to be able to deal with continuous data and to use equivalence classes for knowledge representation as well. In addition, we propose an implementation algorithm of the GBRS by introducing the positive region of GBRS into the PRS framework. The experimental results on benchmark datasets demonstrate that the learning accuracy of the GBRS has been significantly improved compared with the PRS and the traditional NRS. The GBRS also outperforms nine popular or the state-of-the-art feature selection methods. We have open-sourced all the source codes of this article at https://www.cquptshuyinxia.com/GBRS.html, https://github.com/syxiaa/GBRS.
引用
收藏
页码:1719 / 1733
页数:15
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