南京大学学报(自然科学版) ›› 2021, Vol. 57 ›› Issue (1): 141149.doi: 10.13232/j.cnki.jnju.2021.01.015
• • 上一篇
Wenbin Zheng1,3(), Jinjin Li2, Yanlan Zhang1,3, Shujiao Liao2
摘要:
粒度约简是多粒度粗糙集的重要议题,现存的多粒度粗糙集粒度约简方法以考虑各种形式计算多粒度下的正域为主要的研究方法.然而对于多粒度粗糙集,因为同时存在悲观视角与乐观视角,不仅下近似会因悲观、乐观视角而产生差异,视角同样会影响上近似的大小.因此,提出一种可以保持多粒度上下近似不变的粒度约简方法,同时考量多粒度粗糙集的上近似与下近似的粒度重要度,基于重要度设计了用矩阵计算粒度重要度的方法,并提出相应的粒度约简算法.在UCI公开数据集上使用对比算法验证了所提算法的有效性和优越性.
中图分类号:
1 | Qian Y H,Liang J Y,Yao Y Y,et al. MGRS:a multigranulation rough set. Information Sciences,2010,180(6):949-970. |
2 | Huang B,Li H X,Feng G F,et al. Inclusion mea?sure?based multi?granulation intuitionistic fuzzy decision?theoretic rough sets and their application to ISSA. Knowledge?Based Systems,2017,138:220-231. |
3 | Mandal P,Ranadive A S. Fuzzy multi?granulation decision?theoretic rough sets based on fuzzy preference relation. Soft Computing,2019,23(1):85-99. |
4 | Bo C X,Zhang X H,Shao S T,et al. Multi?granulation neutrosophic rough sets on a single domain and dual domains with applications. Symmetry,2018,10(7):296. |
5 | Lin G P,Qian Y H,Li J J. NMGRS:neighborhood?based multigranulation rough sets. International Journal of Approximate Reasoning,2012,53(7):1080-1093. |
6 | Dou H L,Yang X B,Fan J Y,et al. The models of variable precision multigranulation rough sets∥Li T. Rough Sets and Knowledge Technology. Springer Berlin Heidelberg,2012:465-473. |
7 | 张明,唐振民,徐维艳等. 可变多粒度粗糙集模型. 模式识别与人工智能,2012,25(4):709-720. |
Zhang M,Tang Z M,Xu W Y,et al. Variable multigranulation rough set model. Pattern Recognition and Artificial Intelligence,2012,25(4):709-720. | |
8 | 胡善忠,徐怡,何明慧等. 多粒度粗糙集粒度约简的高效算法. 计算机应用,2017,37(12):3391-3396. |
Hu S Z,Xu Y,He M H,et al. Effective algorithm for granulation reduction of multi?granulation rough set. Journal of Computer Applications,2017,37(12):3391-3396. | |
9 | 邓大勇,黄厚宽. 多粒度粗糙集的双层绝对约简. 模式识别与人工智能,2016,29(11):969-975. |
Deng D Y,Huang H K. Double?level absolute reduction for multi?granulation rough sets. Pattern Recognition and Artificial Intelligence,2016,29(11):969-975. | |
10 | 孟慧丽,马媛媛,徐久成. 基于信息量的悲观多粒度粗糙集粒度约简. 南京大学学报(自然科学),2015,51(2):343-348. |
Meng H L,Ma Y Y,Xu J C. The granularity reduction of pessimistic multi?granulation rough set based on the information quantity. Journal of Nanjing University (Natural Science),2015,51(2):343-348. | |
11 | 汪小燕,郭云婷,申元霞. 可变多粒度粗糙集粒度约简研究. 哈尔滨师范大学学报(自然科学),2019,35(1):24-30. |
Wang X Y,Guo Y T,Shen Y X. Research on granularity reduction of variable multi?granulation rough set. Natural Science Journal of Harbin Normal University,2019,35(1):24-30. | |
12 | 张艳芹. 模糊多粒度粗糙集约简方法研究. 武汉理工大学学报,2014,36(8):133-137. |
Zhang Y Q. Researching on reduct of fuzzy multigranulation rough set. Journal of Wuhan University of Technology,2014,36(8):133-137. | |
13 | 于莹莹. 全序优势关系下区间信息系统多粒度粗糙集的粒度约简. 广西师范学院学报:自然科学版,2017,34(1):66-70. |
Yu Y Y. Granularity reduction of multi?size rough set in interval information systems under the relationship of full sequence advantage. Journal of Guangxi Teachers Education University:Natural Science Edition,2017,34(1):66-70. | |
14 | 桑妍丽,钱宇华. 一种悲观多粒度粗糙集中的粒度约简算法. 模式识别与人工智能,2012,25(3):361-366. |
Sang Y L,Qian Y H. A granular space reduction approach to pessimistic multi?granulation rough sets. Pattern Recognition and Artificial Intelligence,2012,25(3):361-366. | |
15 | Yu P Q,Li J J,Lin G P. Vector?based approaches for computing approximations in multigranulation rough set. The Journal of Engineering,2018,2018(16):1538-1543. |
16 | Guo G D,Wang H,Bell D,et al. KNN model?based approach in classification∥Meersman R,Tari Z,Schmidt D C. On the Move to Meaningful Internet Systems 2003:CoopIS,DOA and ODBASE. Springer Berlin Heidelberg,2003:986-996. |
17 | Breiman L I,Friedman J H,Olshen R A,et al. Classification and regression trees. Biometrics,1984,40(3):358. |
18 | Saunders C,Stitson M O,Weston J,et al. Support vector machine.Machine learning models and algorithms for big data classification. Bsoton:Springer,2016:207-235. |
[1] | 任睿,张超,庞继芳. 有限理性下多粒度q⁃RO模糊粗糙集的最优粒度选择及其在并购对象选择中的应用[J]. 南京大学学报(自然科学版), 2020, 56(4): 452-460. |
[2] | 孟慧丽*,马媛媛,徐久成. 基于信息量的悲观多粒度粗糙集粒度约简[J]. 南京大学学报(自然科学版), 2015, 51(2): 343-348. |
[3] | 莫京兰1,2,吕跃进2,李金海3. 不完备序信息系统的模型扩展及其属性约简[J]. 南京大学学报(自然科学版), 2015, 51(2): 430-437. |
[4] | 邱媛媛1,许 阳2. 高斯叠代法研究相控阵栅瓣的优化*[J]. 南京大学学报(自然科学版), 2013, 49(6): 782-. |
[5] | 王丽娟 1.2**,杨习贝1.2,杨静宇1,吴陈2 . 一种新的不完备多粒度粗糙集*[J]. 南京大学学报(自然科学版), 2012, 48(4): 436-444. |
|