机器学习研究趋势的文献计量学挖掘

AI Pub Date : 2024-01-19 DOI:10.3390/ai5010012
Lars Lundberg, Martin Boldt, Anton Borg, Håkan Grahn
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引用次数: 0

摘要

我们提出了一种方法,包括工具支持,用于对大型动态研究领域的趋势进行文献计量学挖掘。该方法适用于 2013 年至 2022 年的机器学习研究领域。我们分析了 Scopus 中的 398 782 篇文献。在 Python 程序和现有分类标准的帮助下,四位专家定义了包含机器学习领域 26 个研究方向的分类标准。我们分析了分类法中各研究方向在生产率、增长率和引用率方面的趋势。我们的结果显示,应用和算法这两个方向的规模最大,而卷积神经网络是增长最快、平均每篇文献被引用次数最多的方向。事实还证明,增长率与每篇文献的平均被引次数之间存在明显的相关性,即增长快的研究方向的文献被引次数更多。我们还分析了四个地理区域(北美、欧洲、金砖国家和世界其他地区)的机器学习研究趋势。在考虑的时间段内,所有地区的文献数量大致相同。金砖国家的增长率最高,平均而言,北美地区每篇文献的被引次数最高。利用我们的工具和方法,我们预计可以在相对较短的时间内对其他大型动态研究领域进行类似的研究。
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Bibliometric Mining of Research Trends in Machine Learning
We present a method, including tool support, for bibliometric mining of trends in large and dynamic research areas. The method is applied to the machine learning research area for the years 2013 to 2022. A total number of 398,782 documents from Scopus were analyzed. A taxonomy containing 26 research directions within machine learning was defined by four experts with the help of a Python program and existing taxonomies. The trends in terms of productivity, growth rate, and citations were analyzed for the research directions in the taxonomy. Our results show that the two directions, Applications and Algorithms, are the largest, and that the direction Convolutional Neural Networks is the one that grows the fastest and has the highest average number of citations per document. It also turns out that there is a clear correlation between the growth rate and the average number of citations per document, i.e., documents in fast-growing research directions have more citations. The trends for machine learning research in four geographic regions (North America, Europe, the BRICS countries, and The Rest of the World) were also analyzed. The number of documents during the time period considered is approximately the same for all regions. BRICS has the highest growth rate, and, on average, North America has the highest number of citations per document. Using our tool and method, we expect that one could perform a similar study in some other large and dynamic research area in a relatively short time.
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AI
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