[1]高文华,梁吉业,王宝丽,等.非完备决策信息系统中的不确定性度量[J].智能系统学报,2019,14(06):1100-1110.[doi:10.11992/tis.201905052]
 GAO Wenhua,LIANG Jiye,WANG Baoli,et al.Uncertainty measure in incomplete decision information system[J].CAAI Transactions on Intelligent Systems,2019,14(06):1100-1110.[doi:10.11992/tis.201905052]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年06期
页码:
1100-1110
栏目:
出版日期:
2019-11-05

文章信息/Info

Title:
Uncertainty measure in incomplete decision information system
作者:
高文华1 梁吉业2 王宝丽3 庞天杰1
1. 太原师范学院 计算机科学与技术系, 山西 晋中 030619;
2. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006;
3. 运城学院 数学与信息技术学院, 山西 运城 044000
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
关键词:
非完备决策系统相容关系知识粒度不确定性度量条件熵属性权重最小条件熵原则多属性决策方法
Keywords:
incomplete decision systemtolerance relationknowledge granularityuncertainty measureconditional entropyattribute weightminimum conditional entropy principlemulti-attribute decision-making method
分类号:
TP301
DOI:
10.11992/tis.201905052
摘要:
针对粗糙集数据分析中的不确定性度量问题。本文首先构造一种新型的考虑条件属性缺失度的目标概念条件熵和决策知识条件熵。在此基础上,提出基于条件熵的属性权重确定技术和最小条件熵非完备属性取值补充方法,用以解决属性权重完全未知的非完备多属性决策问题。应用实例分析表明:该方法能有效结合粗粒度的初步分级信息,客观地确定决策因素取值,具有很强的解释意义,得到的决策结果更为合理有效。
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.

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备注/Memo

备注/Memo:
收稿日期:2019-05-27。
基金项目:国家自然科学基金项目(61703363);山西省重点实验室开放课题基金项目(CICIP2018008)
作者简介:高文华,女,1994年生,硕士研究生,主要研究方向为智能计算与数据建模;梁吉业,男,1962年生,教授,博士生导师,主要研究方向为粒计算、数据挖掘与机器学习。主持国家863计划项目2项、国家自然科学基金项目7项(其中重点项目2项)、国家973计划前期研究专项1项,获山西省自然科学一等奖2项,2014-2018入选爱思唯尔中国高被引学者榜单。发表学术论文200余篇;王宝丽,女,1982年生,副教授,博士,主要研究方向为粒计算、智能决策。主持国家自然科学基金项目1项、参与国家级项目2项、省部级科研及教研项目多项。发表学术论文20余篇
通讯作者:梁吉业.E-mail:ljy@sxu.edu.cn
更新日期/Last Update: 2019-12-25