贝叶斯网络研究现状与发展趋势的文献计量分析
The Bibliometric Analysis of Current Studies and Developing Trends on Bayesian Network Research

DOI: 10.12677/CSA.2020.103052, PP. 493-504

Keywords: 贝叶斯网络，图谱分析，CiteSpace，研究脉络
Bayesian Network, Map Analysis, Citespace, Research Context

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Abstract:

本文以web of science中近10年2930篇与贝叶斯网络有关的文献为研究对象，基于文献计量内容分析方法系统地回顾了国内外在贝叶斯网络领域的关注点、研究脉络的发展规律、存在的共性与差异性和研究现状。研究发现，截至目前，特别是在神经网络盛行的现在，贝叶斯网络可以凭借其具有较强的数学可解释性，在智能计算领域的贡献不断深化且具有极大的潜力。分析结果有助于为我国贝叶斯网络研究领域的学者提供研究现状及进展的参考。
In this paper, 2,930 literatures related to Bayesian network in the recent 10 years in the web of science were taken as the research object. Based on the literature metrological content analysis method, the focus, development rules of research context, existing commonalities and differences, and research status at home and abroad were systematically reviewed. The study found that, as of now, especially in the prevalence of neural networks, Bayesian networks can be deepened and have great potential because of their strong mathematical interpretability. The analysis results are helpful to provide reference for the research status and progress of scholars in the field of Bayesian network research in China.

References

[1]	曹杰. 贝叶斯网络结构学习与应用研究[D]: [博士学位论文]. 合肥: 中国科学技术大学, 2017.
[2]	乔萨础拉, 努尔布力, 苏芮. 数据可视分析研究现状与发展趋势的图谱分析[J]. 现代电子技术, 2018, 41(14): 161-165+169.
[3]	陈悦, 陈超美, 刘则渊, 胡志刚, 王贤文. CiteSpace知识图谱的方法论功能[J]. 科学学研究, 2015, 33(2): 242-253.
[4]	Chaturvedi, I., Ragusa, E., Gastaldo, P., et al. (2017) Bayesian Network based Extreme Learning Machine for Subjectivity Detection. Journal of the Franklin Institute, 355, 1780-1797. https://doi.org/10.1016/j.jfranklin.2017.06.007
[5]	崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10): 3068-3090.
[6]	Anton-Sanchez, L., Larra？aga, P., Benavides-Piccione, R., Fernaud, I., De Felipe, J. and Bielza, C. (2017) Three Dimensional Spatial Modeling of Spines along Dendritic Networks in Human Cortical Pyramidal Neurons. PLoS ONE, 12, e0180400. https://doi.org/10.1371/journal.pone.0180400
[7]	Zhang, J., Ahlbrand, B., Malik, A., et al. (2016) A Visual Analytics Framework for Microblog Data Analysis at Multiple Scales of Aggregation. Computer Graphics Forum, 35, 441-450. https://doi.org/10.1111/cgf.12920
[8]	Lau, C.L. and Smith, C.S. (2016) Bayesian Networks in Infectious Disease Eco-Epidemiology. Reviews on Environmental Health, 31, 173-177. https://doi.org/10.1515/reveh-2015-0052
[9]	Karimi, I. and Salahshoor, K. (2012) A New Fault Detection and Diagnosis Approach for a Distillation Column Based on a Combined PCA and ANFIS Scheme. 2012 24th Chinese Control and Decision Conference, Taiyuan, 23-25 May 2012, 3408-3413. https://doi.org/10.1109/CCDC.2012.6244542
[10]	Cai, B., Huang, L. and Xie, M. (2017) Bayesian Networks in Fault Diagnosis. IEEE Transactions on Industrial Informatics, 13, 2227-2240. https://doi.org/10.1109/TII.2017.2695583
[11]	邱均平, 韩雷. 近十年来我国知识工程研究进展与趋势[J]. 情报科学, 2016, 34(6): 3-9.
[12]	耿杨, 邵苏杰, 郭少勇, 喻鹏. 基于可见损伤持续时间贝叶斯网络的视频QoE评估方法[J]. 通信学报, 2017, 38(6): 136-141.
[13]	李玉兰. 基于贝叶斯网络的列控车载设备故障诊断研究[D]: [硕士学位论文]. 北京: 北京交通大学, 2016.
[14]	陈晓艳, 董朝轶. 动态贝叶斯网络结构搜索法辨识生物神经网络连接[J]. 生命科学研究, 2017, 21(6): 527-533.
[15]	王双成, 高瑞, 杜瑞杰. 具有超父结点时间序列贝叶斯网络集成回归模型[J]. 计算机学报, 2017, 40(12): 2748-2761.
[16]	赵建喆. 具有认知特性的贝叶斯网络结构学习方法研究[D]: [博士学位论文]. 沈阳: 东北大学, 2015.

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