Machine Learning: A Science Mapping Analysis

Machine Learning: A Science Mapping Analysis

Citation Author(s):
Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales
Submitted by:
Juan Rincon-Patino
Last updated:
Tue, 11/12/2019 - 10:38
DOI:
10.21227/H2337Z
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Abstract: 

Machine learning is becoming increasingly important for companies and the scientific community. It allows us to generate solutions for several problems faced by society. In this study, we perform a science mapping analysis on the machine learning research, in order to provide an overview of the scientific work during the last decade in this area and to show trends that could be the basis for future developments in the field of computer science. This study was carried out using the CiteSpace and SciMAT tools based on results from Scopus and Clarivate Web of Science. This analysis shows how the field has evolved, by highlighting the most notable authors, institutions, keywords, countries, categories, and journals. The results obtained through the analysis provide information on tendencies and on the possible future of machine learning, particularly in areas such as health, biology and banking, where machine learning is an important tool to generate solutions.

Instructions: 

The data includes two projects created in SciMAT and CiteSpace, with their respective bibliographical references used as input, to carry out a bibliometric analysis of the evolution of machine learning during the last years.

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[1] Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales, "Machine Learning: A Science Mapping Analysis", IEEE Dataport, 2018. [Online]. Available: http://dx.doi.org/10.21227/H2337Z. Accessed: Nov. 19, 2019.
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doi = {10.21227/H2337Z},
url = {http://dx.doi.org/10.21227/H2337Z},
author = {Juan Rincon-Patino; Gustavo Ramirez-Gonzalez; Juan Carlos Corrales },
publisher = {IEEE Dataport},
title = {Machine Learning: A Science Mapping Analysis},
year = {2018} }
TY - DATA
T1 - Machine Learning: A Science Mapping Analysis
AU - Juan Rincon-Patino; Gustavo Ramirez-Gonzalez; Juan Carlos Corrales
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PB - IEEE Dataport
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Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales. (2018). Machine Learning: A Science Mapping Analysis. IEEE Dataport. http://dx.doi.org/10.21227/H2337Z
Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales, 2018. Machine Learning: A Science Mapping Analysis. Available at: http://dx.doi.org/10.21227/H2337Z.
Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales. (2018). "Machine Learning: A Science Mapping Analysis." Web.
1. Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales. Machine Learning: A Science Mapping Analysis [Internet]. IEEE Dataport; 2018. Available from : http://dx.doi.org/10.21227/H2337Z
Juan Rincon-Patino, Gustavo Ramirez-Gonzalez, Juan Carlos Corrales. "Machine Learning: A Science Mapping Analysis." doi: 10.21227/H2337Z