[1]YANG Jie,WANG Guoyin,ZHANG Qinghua.Evaluation model of rough granular structure based on knowledge distance[J].CAAI Transactions on Intelligent Systems,2020,15(1):166-174.[doi:10.11992/tis.201904037]
Copy

Evaluation model of rough granular structure based on knowledge distance

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 15 Number of periods: 2020 1 Page number: 166-174 Column: 人工智能院长论坛 Public date: 2020-01-05
Title:
Evaluation model of rough granular structure based on knowledge distance
Author(s):
YANG Jie1; 2;  WANG Guoyin1; 2; 3;  ZHANG Qinghua1; 2; 3
1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;
2. School of computer science and technology, Chongqing University of Posts and Telecommunications, Chongqing 4000
Keywords:
rough granular structure; knowledge distance; uncertainty measure; evaluation model; granular computing; rough sets; constraint condition; uncertainty measure
CLC:
TP311
DOI:
10.11992/tis.201904037
Abstract:
In the theory of granular computing (GrC), different granular structures are generated by various grain calculation mechanisms. In rough sets, for the same information table, different attribute adding sequence produces different sequential hierarchical structure, namely the rough granular structure. In rough granular structure, various order of attribute acquisition leads to different effects of solving uncertain problems. This leads to an interesting research topic: how to effectively evaluate the rough granular structures. This problem is solved from the perspective of knowledge distance in the paper. Firstly, the knowledge distance mentioned in our previous works is introduced and then a rough approximation space distance (RASD) is proposed to measure the difference between rough approximate space. On the basis of the knowledge distance mentioned above, the characteristic of rough granular structure (RGS) is investigated. In the rough granular structure, when solving uncertain problem, we expect to to maximize the uncertainty reduction as much as possible by using smaller knowledge space. Then, based on this idea and the above conclusions, an evaluation parameter λ is introduced under the constraint of attribute cost, and further, an evaluation model of rough granular structure is established.Finally,the effectiveness of this method is verified by an example.
References:
[1] ZADEH L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy sets and systems, 1997, 90(2): 111–127.
[2] PEDRYCZ W, SKOWRON A, KREINOVICH V. Handbook of granular computing[M]. Chichester: John Wiley & Sons, 2008: 719-740.
[3] PEDRYCZ W, AL-HMOUZ R, MORFEQ A, et al. The design of free structure granular mappings: the use of the principle of justifiable granularity[J]. IEEE transactions on cybernetics, 2013, 43(6): 2105–2113.
[4] WANG Guoyin, YANG Jie, XU Ji. Granular computing: from granularity optimization to multi-granularity joint problem solving[J]. Granular computing, 2017, 2(3): 105–120.
[5] YAO Yiyu. Perspectives of granular computing[C]//Proceedings of 2015 IEEE International Conference on Granular Computing. Beijing, China, 2005: 85-90.
[6] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3): 338–353.
[7] PAWLAK Z. Rough sets[J]. International journal of computer & information sciences, 1982, 11(5): 341–356.
[8] 张钹, 张铃. 问题求解理论及应用[M]. 北京: 清华大学出版社, 1990.
[9] 李德毅, 孟海军, 史雪梅. 隶属云和隶属云发生器[J]. 计算机研究与发展, 1995(6): 15–20
LI Deyi, MENG Haijun, SHI Xuemei. Membership clouds and membership cloud generators[J]. Journal of computer research and development, 1995(6): 15–20
[10] 苗夺谦, 李德毅, 姚一豫, 等. 不确定性与粒计算[M]. 北京: 科学出版社, 2011.
[11] 徐计, 王国胤, 于洪. 基于粒计算的大数据处理[J]. 计算机学报, 2015(8): 1497–1517
XU Ji, WANG Guoyin, YU Hong. Review of big data processing based on granular computing[J]. Chinese journal of computers, 2015(8): 1497–1517
[12] YAO Yiyu. Three-way decisions with probabilistic rough sets[J]. Information sciences, 2010, 180(3): 341–353.
[13] QIAN Yuhua, LIANG Jiye, YAO Yiyu, et al. MGRS: a multi-granulation rough set[J]. Information sciences, 2010, 180(6): 949–970.
[14] QIAN Yuhua, LIANG Jiye, DANG Chuangyin. Incomplete multigranulation rough set[J]. IEEE transactions on systems, man, and cybernetics-part A: systems and humans, 2010, 40(2): 420–431.
[15] QIAN Yuhua, CHENG Honghong, WANG Jieting, et al. Grouping granular structures in human granulation intelligence[J]. Information sciences, 2017, 382-383: 150–169.
[16] 张钹, 张铃. 问题求解理论及应用[M]. 北京: 清华大学出版社, 1990.
[17] ZHANG Ling, ZHANG Bo. The structure analysis of fuzzy sets[J]. International journal of approximate reasoning, 2005, 40(1/2): 92–108.
[18] ZHANG Ling, ZHANG Bo. Fuzzy reasoning model under quotient space structure[J]. Information sciences, 2005, 173(4): 353–364.
[19] WIERMAN M J. Measuring uncertainty in rough set theory[J]. International journal of general systems, 1999, 28(4/5): 283–297.
[20] 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.
[21] YAO Yiyu, ZHAO Liquan. A measurement theory view on the granularity of partitions[J]. Information sciences, 2012, 213: 1–13.
[22] LAWVERE F W. Metric spaces, generalized logic, and closed categories[J]. Rendiconti del seminario matematico e fisico di milano, 1973, 43(1): 135–166.
[23] RUBNER Y, GUIBAS L, TOMASI C. The earth mover’s distance, multi-dimensional scaling, and color-based image retrieval[C]//Proceedings of the ARPA Image Understanding Workshop. 1997: 661-668.
[24] RUBNER Y, TOMASI C, GUIBAS L J. The earth mover’s distance as a metric for image retrieval[J]. International journal of computer vision, 2000, 40(2): 99–121.
[25] 郭增晓, 米据生. 粗糙模糊集的模糊性度量[J]. 模糊系统与数学, 2005, 19(4): 135–140
GUO Zengxiao, MI Jusheng. An uncertainty measure in rough fuzzy sets[J]. Fuzzy systems and mathematics, 2005, 19(4): 135–140
[26] QIN Huani, LUO Darong. New uncertainty measure of rough fuzzy sets and entropy weight method for fuzzy-target decision-making tables[J]. Journal of applied mathematics, 2014, 2014: 487036.
[27] HU J, PEDRYCZ W, WANG G. A roughness measure of fuzzy sets from the perspective of distance[J]. International journal of general systems, 2016, 45(3): 352–367.
[28] SUN Bingzhen, MA Weimin. Uncertainty measure for general relation-based rough fuzzy set[J]. Kybernetes, 2013, 42(6): 979–992.
[29] DAI Jianhua, WANG Wentao, XU Qing. An uncertainty measure for incomplete decision tables and its applications[J]. IEEE transactions on cybernetics, 2013, 43(4): 1277–1289.
[30] QIAN Y H, LIANG J Y, DANG C Y, et al. Knowledge granulation and knowledge distance in a knowledge base[J]. International journal of approximate reasoning, 2011, 19(2): 263–264.
[31] QIAN Yuhau, LI Yebin, LIANG Jiye, et al. Fuzzy granular structure distance[J]. IEEE transactions on fuzzy systems, 2015, 23(6): 2245–2259.
[32] LIANG Jiye, LI Ru, QIAN Yuhau. Distance: a more comprehensible perspective for measures in rough set theory[J]. Knowledge-based systems, 2012, 27: 126–136.
[33] YANG Xibei, QIAN Yuhua, YANG Jingyu. On characterizing hierarchies of granulation structures via distances[J]. Fundamenta informaticae, 2013, 123(3): 365–380.
[34] YANG Xibei, SONG Xiaoning, SHE Yanhong, et al. Hierarchy on multigranulation structures: a knowledge distance approach[J]. International journal of general systems, 2013, 42(7): 754–773.
[35] YANG Jie, WANG Guoyin, ZHANG Qinghua. Knowledge distance measure in multigranulation spaces of fuzzy equivalence relations[J]. Information sciences, 2018, 448-449: 18–35.
[36] YANG Jie, WANG Guoyin, LI Xukun. Multi-granularity similarity measure of cloud concept[C]//International Joint Conference on Rough Sets. Santiago, Chile, 2016: 318-330.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems