[1]GAO Wenhua,LIANG Jiye,WANG Baoli,et al.Uncertainty measure in incomplete decision information system[J].CAAI Transactions on Intelligent Systems,2019,14(6):1100-1110.[doi:10.11992/tis.201905052]
Copy

Uncertainty measure in incomplete decision information system

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 6 Page number: 1100-1110 Column: 学术论文—机器感知与模式识别 Public date: 2019-11-05
Title:
Uncertainty measure in incomplete decision information system
Author(s):
GAO Wenhua1;  LIANG Jiye2;  WANG Baoli3;  PANG Tianjie1
1. Department of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China;
2. Key Laboratory of Ministry of Education for Computational Intelligence and Chinese Information Processing, Taiyuan 030006, China;
3. School of Mathematics and Information Technology, Yuncheng University, Yuncheng 044000, China
Keywords:
incomplete decision system; tolerance relation; knowledge granularity; uncertainty measure; conditional entropy; attribute weight; minimum conditional entropy principle; multi-attribute decision-making method
CLC:
TP301
DOI:
10.11992/tis.201905052
Abstract:
In order to solve uncertainty measure problem in data analysis of rough set, this study first constructs a new type of conditional entropy of the objective concept and conditional entropy of decision knowledge, with consideration of the degree of missing of conditional attributes, and moreover, proposes the conditional entropy-based attribute weight determination technique and a complementary method for incomplete attributes with minimum conditional entropy, so as to solve a kind of incomplete multi-attribute decision-making problem whose attribute weight is completely unknown. The real practical application shows that the proposed method can effectively combine coarse-grained preliminary classification information to objectively determine the value of decision factors, having strong explanatory significance, and the obtained decision results are more reasonable and effective.
References:
[1] PAWLAK Z. Rough sets[J]. International journal of computer and information science, 1982, 11(5):341-356.
[2] 苗夺谦, 张清华, 钱宇华, 等. 从人类智能到机器实现模型-粒计算理论与方法[J]. 智能系统学报, 2016, 11(6):743-757 MIAO Duoqian, ZHANG Qinghua, QIAN Yuhua, et al. From human intelligence to machine implementation model:theories and applications based on granular computing[J]. CAAI transactions on intelligent systems, 2016, 11(6):743-757
[3] 梁吉业, 钱宇华, 李德玉, 等. 大数据挖掘的粒计算理论与方法[J]. 中国科学(信息科学), 2015, 45(11):1355-1369 LIANG Jiye, QIAN Yuhua, LI Deyu, et al. Theory and method of granular computing for big data mining[J]. Scientia sinica informationis, 2015, 45(11):1355-1369
[4] 王国胤, 张清华, 马希骜, 等. 知识不确定性问题的粒计算模型[J]. 软件学报, 2011, 22(4):676-694 WANG Guoyin, ZHANG Qinghua, MA Xi’ao, et al. Granular computing models for knowledge uncertainty[J]. Journal of software, 2011, 22(4):676-694
[5] PAWLAK Z. Vagueness and uncertainty:a rough set perspective[J]. Computational intelligence, 1995, 11(2):227-232.
[6] DüNTSCH I, GEDIGA G. Uncertainty measures of rough set prediction[J]. Artificial intelligence, 1998, 106(1):109-137.
[7] WIERMAN M J. Measuring uncertainty in rough set theory[J]. International journal of general systems, 1999, 28(4/5):283-297.
[8] CHAKRABARTY K, BISWAS R, NANDA S. Fuzziness in rough sets[J]. Fuzzy sets and systems, 2000, 110(2):247-251.
[9] 苗夺谦, 王珏. 粗糙集理论中知识粗糙性与信息熵关系的讨论[J]. 模式识别与人工智能, 1998, 11(1):34-40 MIAO Duoqian, WANG Jue. On the relationships between information entropy and roughness of knowledge in rough set theory[J]. Pattern recognition and artificial intelligence, 1998, 11(1):34-40
[10] LIANG Jiye, WANG Junhong, QIAN Yuhua. A new measure of uncertainty based on knowledge granulation for rough sets[J]. Information sciences, 2009, 179(4):458-470.
[11] 王国胤, 于洪, 杨大春. 基于条件信息熵的决策表约简[J]. 计算机学报, 2002, 25(7):759-766 WANG Guoyin, YU Hong, YANG Dachun. Decision table reduction based on conditional information entropy[J]. Chinese journal of computers, 2002, 25(7):759-766
[12] 黄国顺, 文翰. 基于严凹函数的粗糙集不确定性度量[J]. 软件学报, 2018, 29(11):3484-3499 HUANG Guoshun, WEN Han. Uncertainty measures of rough set based on strictly concave functions[J]. Journal of software, 2018, 29(11):3484-3499
[13] 黄国顺, 曾凡智, 陈广义, 等. 基于严凸函数的知识粒度与相对粒度[J]. 模式识别与人工智能, 2013, 26(10):897-908 HUANG Guoshun, ZENG Fanzhi, CHEN Guangyi, et al. Knowledge granularity and relative granularity based on strictly convex function[J]. Pattern recognition and artificial intelligence, 2013, 26(10):897-908
[14] LIANG J, SHI Z, LI D, et al. Information entropy, rough entropy and knowledge granulation in incomplete information systems[J]. International journal of general systems, 2006, 35(6):641-654.
[15] DAI Jianhua, XU Qing. Approximations and uncertainty measures in incomplete information systems[J]. Information sciences, 2012, 198:62-80.
[16] QIAN Yuhua, LIANG Jiye, WANG Feng. A new method for measuring the uncertainty in incomplete information systems[J]. International journal of uncertainty, fuzziness and knowledge-based systems, 2009, 17(6):855-880.
[17] QIAN Yuhua, LIANG Jiye, DANG Chuangyin. Consistency measure, inclusion degree and fuzzy measure in decision tables[J]. Fuzzy sets and systems, 2008, 159(18):2353-2377.
[18] DAI Jianhua, XU Qing, WANG Wentao, et al. Conditional entropy for incomplete decision systems and its application in data mining[J]. International journal of general systems, 2012, 41(7):713-728.
[19] 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.
[20] YANG Jiping, QIU Wanhua. A measure of risk AND A decision-making model based on expected utility and entropy[J]. European journal of operational research, 2005, 164(3):792-799.
[21] 吕跃进, 张旭娜, 韦碧鹏. 基于优势关系粗糙集的模糊综合评价的权重确定[J]. 统计与决策, 2012(20):44-46 LV Yuejin, ZHANG Xuna, WEI Bipeng. Rough set based on dominance relation of fuzzy comprehensive evaluation of the weight[J]. Control and decision, 2012(20):44-46
[22] 李佳, 梁吉业, 庞天杰. 一种基于优势粗糙集的多属性决策排序方法[J]. 南京大学学报(自然科学), 2016, 52(5):844-852 LI Jia, LIANG Jiye, PANG Tianjie. A sorting method of multi-attribute decision making based on dominance rough set theory[J]. Journal of Nanjing University (Natural Sciences), 2016, 52(5):844-852
[23] KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information sciences, 1998, 112(1/2/3/4):39-49.
[24] 王利东, 田晓娟, 杨艳冰. 基于熵权与优势关系的教学效果评价方案[J]. 数学的实践与认识, 2014, 44(10):8-12 WANG Lidong, TIAN Xiaojuan, YANG Yanbing. The comprehensive evaluation of teaching based on entropy weight and dominance relation[J]. Mathematics in practice and theory, 2014, 44(10):8-12
[25] 翁世洲, 吕跃进, 莫京兰. 基于优势关系的排序模型及其保序性约简理论[J]. 广西师范大学学报(自然科学版), 2013, 31(3):37-44 WENG Shizhou, LV Yuejin, MO Jinglan. Ranking model and its order-preserving reduction theory based on dominance relations[J]. Journal of Guangxi Normal University (Natural Science Edition), 2013, 31(3):37-44
[26] 施玉杰, 杨宏志, 徐久成. 先验概率优势关系下的粗糙集模型研究[J]. 南京大学学报(自然科学版), 2016, 52(5):899-907 SHI Yujie, YANG Hongzhi, XU Jiucheng. The study of rough set model under α-prior probability dominance relation[J]. Journal of Nanjing University (Natural Sciences), 2016, 52(5):899-907
[27] 李佳, 梁吉业, 庞天杰. 基于加权α优势关系的多属性决策排序方法[J]. 模式识别与人工智能, 2017, 30(8):761-768 LI J, LIANG Jiye, PANG Tianjie. Sorting method of multi-attribute decision making based on weighted α dominance relation[J]. Pattern recognition and artificial intelligence, 2017, 30(8):761-768
Similar References:

Memo

-

Last Update: 2019-12-25

Copyright © CAAI Transactions on Intelligent Systems