Abstract
Background: Machine learning is becoming increasingly important for companies and the scientific community. In this study, we perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 2007-2017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus. This analysis shows the strategic diagrams of evolution and a set of thematic networks. The results provide information on broad tendencies of machine learning. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Conclusions: Some of the conclusions exposed in this study show that classification algorithms have been widely studied and represent a relevant tool for generating different machine learning applications. Nonetheless, regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example.
Highlights
The machine learning field researches different human learning processes, the theoretical analysis of possible learning algorithms and methods for several application domains[1]
We generated a diagram for each period of the study
Exposing emerging trends in the field of machine learning allows researchers to increase their understanding of the changes and the evolution over time of this research field
Summary
The machine learning field researches different human learning processes, the theoretical analysis of possible learning algorithms and methods for several application domains[1]. We perform a science mapping analysis to explore machine learning research. We perform a bibliometric analysis on machine learning research, in order to provide an overview of the scientific work during the period 20072017 in this area and to show trends that could be the basis for future developments in the field. Methods: This study is carried out using the SciMAT tool based on results extracted from Scopus This analysis shows the strategic diagrams of evolution and a set of thematic networks. Results: The results show that SciMAT is a useful tool to carry out a science mapping analysis, and emphasizes the premise that machine learning has boundless applications and will continue to be an interesting research field in the future. Regression algorithms are becoming increasingly important in the scientific community, allowing the generation of solutions to predict diseases, sales, and yields, for example
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