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Bibliometric analysis of support vector machines research trend: a case study in China

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Abstract

Support vector machine (SVM) is a widely used algorithm in the field of machine learning, and it is a research hotspot in the field of data mining. In order to fully understand the historical progress and current situation of SVM researches, as well as its future development trend in China, this paper conducts a comprehensive bibliometric study based on the publications from web of science database by Chinese scholars in this field. First, this paper focuses on some of the basic characteristics of the research publications of SVM in China, including important journals, research institutions and countries/regions, most cited publications, and so on. Then, based on the knowledge mapping software VOSviewer, the cooperation between other countries and China as well as the cooperation between research institutions in China are explored. Finally, VOSviewer based bibliometric visualization graphics are used to identify the changes of the research hotspots in the SVM field. This paper provides a relatively broad perspective for the evaluation of SVM scientific researches, and reveals the development trend in this field.

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Acknowledgements

This manuscript was supported by the Ministry of Education of Humanities and Social Science project (No. 19YJC630208), the Qinglan Project of Jiangsu Province (2019), the National Natural Science Foundation of China (Nos. 71771155, 71571123), and the Natural Science Research Project of Jiangsu Higher Education Institutions (19KJB120008).

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Authors and Affiliations

  1. Business School, Nanjing Audit University, Nanjing, Jiangsu, 211815, China

    Dejian Yu

  2. Business School, Sichuan University, Chengdu, 610064, China

    Zeshui Xu

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Xizhao Wang

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  1. Dejian Yu
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  2. Zeshui Xu
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  3. Xizhao Wang
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Correspondence to Zeshui Xu.

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Appendix

Appendix

Abbreviation of the journal name in Fig. 7:

Neu:

Neurocomputing

LNICS:

Lecture Notes in Computer Science

SASA:

Spectroscopy and Spectral Analysis

PO:

Plos One

ESWA:

Expert Systems with Applications

Sen.:

Sensors

MPIE:

Mathematical Problems in Engineering

IA:

Ieee Access

PR:

Pattern Recognition

RS:

Remote Sensing

KBS:

Knowledge Based Systems

ITOGARS:

IEEE Transactions on Geoscience and Remote Sensing

NCA:

Neural Computing Applications

JOTB:

Journal of Theoretical Biology

MTAA:

Multimedia Tools and Applications

LJSTAEORS:

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

PAPL:

Protein and Peptide Letters

IJORS:

International Journal of Remote Sensing

CAILS:

Chemometrics and intelligent Laboratory Systems

BBBmc:

Bioinformatics

SR:

Scientific Reports

IS:

Information Sciences

LNIAI:

Lecture Notes in Artificial intelligence

LGARSL:

IEEE Geoscience and Remote Sensing Letters

Ene.:

Energies

PRL:

Pattern Recognition Letters

Mea.:

Measurement

ASC:

Applied Soft Computing

JOARS:

Journal of Applied Remote Sensing

Optik:

Optik

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Yu, D., Xu, Z. & Wang, X. Bibliometric analysis of support vector machines research trend: a case study in China. Int. J. Mach. Learn. & Cyber. 11, 715–728 (2020). https://doi.org/10.1007/s13042-019-01028-y

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  • DOI: https://doi.org/10.1007/s13042-019-01028-y

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