Vol.:(0123456789) 1 3 International Journal of Machine Learning and Cybernetics https://doi.org/10.1007/s13042-019-01028-y ORIGINAL ARTICLE Bibliometric analysis of support vector machines research trend: a case study in China Dejian Yu 1  · Zeshui Xu 2  · Xizhao Wang 3 Received: 20 August 2019 / Accepted: 28 October 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Abstract Support vector machine (SVM) is a widely used algorithm in the feld of machine learning, and it is a research hotspot in the feld 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 feld. 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 feld. This paper provides a relatively broad perspective for the evaluation of SVM scientifc researches, and reveals the development trend in this feld. Keywords Bibliometric analysis · Support vector machines · Co-citation · Co-occurrence · China 1 Introduction The 21st century is the information age of rapid develop- ment. The data volume has exploded and produced a huge amount of data, how to mine the useful knowledge or laws hidden in the massive data is a task of data mining [1]. It is an extraordinary process of revealing implicit, previously unknown and potentially valuable information from a large amount of data in a database [2–4]. There are many methods of data mining, such as Bayesian method, genetic algorithm, artifcial neural network algorithm, decision tree algorithm and so on [5, 6]. The support vector machine (SVM) is a machine learning method based on statistical learning theory and structural risk miniaturization principle [7, 8]. It shows many unique advantages in solving small sample, nonlinear and high-dimensional pattern recognition problems [9, 10]. In addition, it has solid theoretical foundation and simple and straightforward mathematical models. Therefore, SVM has been widely used in many problems such as face recog- nition, time series prediction, and pattern recognition and so on [11, 12]. In recent years, SVM has received more and more attention. It has become one of the hot spots in com- puter science, statistics, and especially data mining. At the same time, academic papers are growing rapidly. To date, thousands of academic papers in this feld have been pub- lished in various international journals. Previous researchers have made some literature reviews on SVM. Mountrakis et al. [13] made a comprehensive and detailed analysis of the important application results of SVM in the feld of remote sensing. It indicates that the most important feature of SVM is that it has a good perfor- mance in terms of prediction accuracy and accuracy even under the condition of limited sample capacity. Compared to other methods such as backpropagation neural networks [14], SVM can achieve higher prediction and classifcation * Zeshui Xu xuzeshui@263.net Dejian Yu yudejian62@126.com Xizhao Wang xizhaowang@ieee.org 1 Business School, Nanjing Audit University, Nanjing, Jiangsu 211815, China 2 Business School, Sichuan University, Chengdu 610064, China 3 College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China