大数据智能决策

于洪; 何德牛; 王国胤; 李劼; 谢永芳

doi:10.16383/j.aas.c180861

大数据智能决策

doi: 10.16383/j.aas.c180861

于洪^1, ,,
何德牛^1,,
王国胤^1,,
李劼^2,,
谢永芳^3,

1.
重庆邮电大学计算智能重庆市重点实验室重庆 400065
2.
中南大学冶金与环境学院长沙 410083
3.
中南大学信息科学与工程学院长沙 410083

基金项目:

国家自然科学基金 61751312

国家自然科学基金 61533020

国家自然科学基金 61876027

详细信息

作者简介:
何德牛  重庆邮电大学计算智能重庆市重点实验室博士研究生.主要研究方向为工业大数据智能决策, 三支决策, 粒计算. E-mail: hedeniu@163.com

王国胤  重庆邮电大学教授, 计算智能重庆市重点实验室主任.主要研究方向为粒计算, 知识发现, 认知计算, 智能信息处理, 大数据智能. E-mail: wanggy@ieee.org

李劼  中南大学教授.主要研究方向为冶金新技术与新材料, 冶金过程计算机仿真优化与智能控制.E-mail: 13808488404@163.com

谢永芳  中南大学教授.主要研究方向为复杂工业过程的建模与控制优化, 分布式鲁棒控制, 知识自动化. E-mail: yfxie@csu.edu.cn

通讯作者:
于洪重庆邮电大学教授.主要研究方向为工业大数据分析与处理, 智能决策, 知识发现, 粒计算, 三支聚类, 智能推荐.本文通信作者. E-mail: yuhong@cqupt.edu.cn

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出版历程
- 收稿日期: 2018-12-29
- 录用日期: 2019-04-11
- 刊出日期: 2020-06-01

Big Data for Intelligent Decision Making

YU Hong^{1
, ,},
HE De-Niu^1
,,
WANG Guo-Yin^1
,,
LI Jie^2
,,
XIE Yong-Fang^3
,

1.
Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065
2.
School of Metallurgy and Environment, Central South University, Changsha 410083
3.
School of Information Science and Engineering, Central South University, Changsha 410083

Funds:

National Natural Science Foundation of China 61751312

National Natural Science Foundation of China 61533020

National Natural Science Foundation of China 61876027

More Information

Author Bio:
HE De-Niu  Ph. D. candidate at Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications. His research interest covers industrial big data for intelligent decision making, three-way decisions and granular computing

WANG Guo-Yin  Professor at Chongqing University of Posts and Telecommunications, Dean of Chongqing Key Laboratory of Computational Intelligence. His research interest covers granular computing, knowledge discovery, cognitive computing, intelligent information processing and big data intelligence

LI Jie  Professor at Central South University. His research interest covers new technologies and materials for metallurgical, computer simulation optimization and intelligent control of metallurgical process

XIE Yong-Fang  Professor at Central South University. His research interest covers modeling and optimal control of complex industrial processes, distributed robust control and knowledge automation

Corresponding author: YU Hong Professor at Chongqing University of Posts and Telecommunications. Her research interest covers industrial big data analysis and processing, intelligent decision making, knowledge discovery, cognitive computing, granular computing, three-way clustering and intelligent recommendation. Corresponding author of this paper

摘要

摘要: 在全球信息化快速发展的背景下, 大数据已经成为一种战略资源.各行各业的决策活动在频度、广度及复杂性上较以往有着本质的不同.决策过程中的不确定性因素增多, 决策分析的难度不断加大.传统的数据分析方法以及基于人工经验的决策已难以满足大数据时代的决策需求, 大数据驱动的智能决策将成为决策研究的主旋律.该文结合大数据特性, 对大数据决策的特点进行了归纳, 并从智能决策支持系统、不确定性处理、信息融合、关联分析和增量分析等方面综述了大数据智能决策的研究与发展现状, 讨论了大数据智能决策依然面临的挑战, 并对一些潜在的研究方向进行了展望分析.
- 大数据 /
- 智能决策 /
- 不确定性 /
- 信息融合 /
- 关联分析 /
- 增量式学习
Abstract: As a result of globalization and informatization, big data has become one kind of important strategic resources. Decision-making activities in all walks of life are different from the past in frequency, breadth and complexity. The difficulty of decision analysis is increased due to the increase of uncertainty factors in the decision-making process. The decision analysis methods based on the traditional data analysis methods or manual experiences are gradually unable to meet the needs of decision-making in the era of big data. We think the intelligent decision making methods based on big data driven will become an important solution. This paper presents the characteristics of big data for intelligent decision making in view of analyzing the features of big data. Some recent theoretic studies and applications of intelligent decision-making systems, uncertainty intelligent decision making, methods based on information fusion, methods based on association analysis and incremental learning are reviewed. The paper also points out the future perspectives and potential research points.
- Big data /
- intelligent decision-making /
- uncertainty /
- information fusion /
- association analysis /
- incremental learning
Recommended by Associate Editor ZHANG Min-Ling
注释:

1) 本文责任编委张敏灵

HTML全文

图 1 跨领域大数据融合范式^[73]

Fig. 1 The paradigm of cross-domain big data fusion^[73]

下载: 全尺寸图片幻灯片

表 1 不同层次下数据融合对比情况表

Table 1 Comparison of data fusion under different levels

融合层次	常用融合机制	优缺点
融合层次	常用融合机制	处理复杂度	信息损失	性能损失	传输负载
数据层	竞争型	高	否	否	高
特征层	竞争, 互补, 合作	中等	是	是	中等
决策层	竞争, 互补, 合作	低到高	是	是	低

下载: 导出CSV

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