Bibliometric Analysis of Machine Learning Knowledge Dynamics From 2012 to 2022

IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1–9

Abstract

This research explores the dynamics of Machine Learning (ML, as a scientific discipline. Using the 'search regime' approach, to trace intellectual structures of ML research, with the primary research question is to uncover the characteristics of the Machine Learning search regime from 2012 to 2022. Web of Science is employed to gather publication data, and numerical analyses are conducted at both micro and macro levels. The study observes a substantial increase in knowledge production since 2012, prompting a detailed investigation of Machine Learning knowledge production from 2018 to 2022. Departments in the field have become more diverse, and knowledge production involves stable international cooperation. The actors in the field, predominantly universities, exhibit stability. Geographically, the field has seen significant development, with concentration in the USA and East Asia, differentiation in disciplines, and a stable fraction of topics. University input dominates the actor landscape in the Machine Learning field.

Authors 1

1. Maarten Voorneveld corresponding Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden University, Leiden Institute of Advanced Computer Science,Netherlands