[1]CHE Xiaoya,LI Leijun,MI Jusheng.Evidence-theory-based numerical characterization of multi-granulation covering rough sets[J].CAAI Transactions on Intelligent Systems,2016,11(4):481-486.[doi:10.11992/tis.201606011]
Copy
Evidence-theory-based numerical characterization of multi-granulation covering rough sets
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
11
Number of periods:
2016 4
Page number:
481-486
Column:
学术论文—知识工程
Public date:
2016-07-25
- Title:
- Evidence-theory-based numerical characterization of multi-granulation covering rough sets
- Keywords:
- rough sets theory; covering; granulation; evidence theory; approximation; characterization
- CLC:
- TP18
- DOI:
- 10.11992/tis.201606011
- Abstract:
- Considering classical multi-granulation rough sets and using the maximal and minimal descriptors of objects in a given universe, this paper proposes four pessimistic multi-granulation covering rough set models, suitable for extensive application. Based on set union and portion functions, the notion of multi-granularity covering connected to a number of coverings and a single granularity partition in the domain are defined. On this basis, belief and plausibility functions from evidence theory are employed to define the relationship between the upper and lower approximations, the belief function, and the likelihood function, and to characterize the set approximations in the four models. Compared with classical multi-granulation rough sets, the pessimistic multi-granulation covering rough set models not only have distinct advantages and combine multi-source information, but also avoid the shortcomings of a narrow application range. Finally, a real example is used to demonstrate the effectiveness of the presented models.
- References:
-
[1] PALAWK Z. Rough set[J]. International journal of computer & information sciences, 1982, 11(5):341-356.
[2] CHEN Degang, KWONG S, HE Qiang, et al. Geometrical interpretation and applications of membership functions with fuzzy rough sets[J]. Fuzzy sets and systems, 2012, 193:122-135.
[3] LIANG Jiye, CHIN K S, Dang Chuangyin, et al. A new method for measuring uncertainty and fuzziness in rough set theory[J]. International journal of general systems, 2002, 31(4):331-342.
[4] LIANG Jiye, WANG Feng, DANG Chaungyin, et al. A group incremental approach to feature selection applying rough set technique[J]. IEEE transactions on knowledge and data engineering, 2014, 26(2):294-308.
[5] TAN Anhui, LI Jinjin, LIN Guoping. Extended results on the relationship between information systems[J]. Information sciences, 2015, 290:156-173.
[6] BONIKOWSKI Z, BRYNIARSKI E, WYBRANIEC-SKARDOWSKA U. Extensions and intentions in the rough set theory[J]. Information sciences, 1998, 107(1/2/3/4):149-167.
[7] FENG Tao, MI Jusheng, WU Weizhi. Covering-based generalized rough fuzzy sets[M]//WANG Guoying, PETERS J F, SKOWRON A, et al. Rough Sets and Knowledge Technology. Berlin Heidelberg:Springer, 2006:208-215.
[8] QIAN Y H, LIANG J Y. Rough set method based on multi-granulations[C]//Proceedings of the 5th IEEE International Conference on Cognitive Informatics. Beijing:IEEE, 2006:297-304.
[9] 徐伟华, 刘士虎, 张文修. 一般二元关系下信息系统知识的粒度描述[J]. 计算机工程与应用, 2011, 47(18):40-44. XU Weihua, LIU Shihu, ZHANG Wenxiu. Granularity representation of knowledge in information system based on general binary-relation[J]. Computer engineering and applications, 2011, 47(18):40-44.
[10] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS:a multi-granulation rough set[J]. Information sciences, 2010, 180(6):949-970.
[11] LIU Caihui, MIAO Duoqian, QIAN Jin. On multi-granulation covering rough sets[J]. International journal of approximate reasoning, 2014, 55(6):1404-1418.
[12] DEMPSTER A P. Upper and lower probability inferences based on a sample from a finite univariate population[J]. Biometrika, 1967, 54(3/4):515-528.
[13] SHAFER G. A mathematical theory of evidence[J]. Technometrics, 1978, 20(1):242.
[14] LIN Guoping, LIANG Jiye, QIAN Yuhua. An information fusion approach by combining multigranulation rough sets and evidence theory[J]. Information sciences, 2015, 314:184-199.
[15] 林国平. 覆盖广义粗糙集与信任函数[J]. 漳州师范学院学报:自然科学版, 2010(2):1-4. LIN Guoping. Connections between covering generization rough set and dempster-shafer theory of evidence[J]. Journal of Zhangzhou normal university:natural science, 2010(2):1-4.
[16] WU Weizhi, MI Jushneg. Knowledge reduction in incomplete information systems based on dempster-shafer theory of evidence[M]//WANG Guoying, PETERS J F, SKOWRON A, et al. Rough Sets and Knowledge Technology. Berlin Heidelberg:Springer, 2006:254-261.
[17] YAO Y Y, LINGRAS P J. Interpretations of belief functions in the theory of rough sets[J]. Information sciences, 1998, 104(1/2):81-106.
[18] CHEN Degang, ZHANG Xiaoxia, LI Wanlu. On measurements of covering rough sets based on granules and evidence theory[J]. Information sciences, 2015, 317:329-348.
[19] CHEN Degang, LI Wanlu, ZHANG Xiao, et al. Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets[J]. International journal of approximate reasoning, 2014, 55(3):908-923.
[20] WU Weizhi, LEUNG Y, ZHANG Wenxiu. Connections between rough set theory and Dempster-Shafer theory of evidence[J]. International journal of general systems, 2002, 31(4):405-430.
[21] 吴伟志, 米据生, 李同军. 无限论域中的粗糙近似空间与信任结构[J]. 计算机研究与发展, 2012, 49(2):327-336. WU Weizhi, MI Jusheng, LI Tongjun. Rough approximation spaces and belief structures in infinite universes of discourse[J]. Journal of computer research and development, 2012, 49(2):327-336.
[22] TAN Anhui, WU Weizhi, LI Jinjin, et al. Evidence-theory-based numerical characterization of multi-granulation rough sets in incomplete information systems[J]. Fuzzy sets and systems, 2016, 294:18-35.
[23] ZAKOWSKI B W. Approximations in the space (u, π)[J]. Demonstratio mathematica, 1983, 16(3):761-769.
- Similar References:
Memo
-
Last Update:
1900-01-01