A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE)
A visualized bibliometric analysis of mapping research trends of machine learning in engineering (MLE)
复制标题
工程机器学习（MLE）研究趋势的可视化文献计量分析
DOI：
10.1016/j.eswa.2021.115728
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发表时间：
2021-08-15
影响因子：
8.5
通讯作者：
Li，Shaofan
中科院分区：
文献类型：
--
作者：
Su，Miao;Peng，Hui;Li，Shaofan
在这项工作中，我们进行了可视化的文献计量学分析，根据2000年至2019年期间发表的Web of Science Core Collection中索引的文章，绘制了工程机器学习（MLE）的研究趋势。利用VOSviewer软件和可视化技术，揭示了MLE研究的研究分布、知识库、研究热点和研究前沿。在过去的20年中，与MLE相关的文献平均增长24.3%。共有来自96个国家的3057篇同行评审论文发表在1299种不同的期刊上。美国是最多产的国家，占总文章的23.73%和总引用的32.25%。最活跃的研究机构是麻省理工学院，有41篇出版物和1079次引用，而《机器学习研究杂志》在MLE领域的引用次数最多。特别是，我们的研究结果表明，“随机森林”，“支持向量机”，“极限学习机”，“深度学习”，“统计学习理论”和“Python机器学习”的研究问题构成了2000年至2019年MLE的知识基础，而研究热点则集中在机器学习基准算法的应用上。突发检测分析结果显示，2010年之后，突发关键词出现数量更多，变化频率更高。本研究对MLE的整体研究趋势提供了一个视角，有助于研究者更好地理解这一研究领域，并预测其动态方向。
In this work, we conducted a visualized bibliometric analysis to map the research trends of machine learning in engineering (MLE) based on articles indexed in the Web of Science Core Collection published between 2000 and 2019. The research distributions, knowledge bases, research hotspots, and research frontiers for MLE studies are revealed by using VOSviewer software and visualization technology. The growth of the literature related to MLE averaged 24.3% in the past two decades. A total of 3057 peer-reviewed papers from 96 countries published in 1299 different journals were identified. The USA was the most productive country, with 23.73% of the overall articles and 32.25% of the overall citations. The most active research organization was MIT, with 41 publications and 1079 citations, and theJournal of Machine Learning Researchhad the largest number of citations in the field of MLE. In particular, our findings indicate that the research issues of “random forests”, “support vector machine”, “extreme learning machine”, “deep learning”, “statistical learning theory”, and “Python machine learning” formed the knowledge bases of MLE from 2000 to 2019, while the research hotspots focused on applications of machine learning benchmark algorithms. Burst detection analysis results showed that more burst keywords emerged and had a higher frequency of change after 2010. This study provides an insight view of the overall research trends of MLE and may help researchers better understand this research field and predict its dynamic directions.