Machine learning and Information Science

contributions to a research and teaching agenda in the brazilian Information Science

Authors

DOI:

https://doi.org/10.5433/1981-8920.2022v27n3p751

Keywords:

Machine learning, Information Science, Algorithms, Web of Science, Teaching

Abstract

Objective: it intends to support the construction of a research and teaching agenda on the subject of machine learning through knowledge of its main applications and most used algorithms.
Methodology: article studies references to machine learning algorithms in indexed documents in the area of Information Science in the Web of Science database.
Results and Conclusions: evaluates 3111 identified documents and concludes that the supervised approach through classification techniques is the most used in the field, with evidence for the Support Vector Machine, Decision Tree, Random Forest algorithms.

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Author Biographies

Dalton Lopes Martins, Universidade de Brasília - UnB

PhD in Information Sciences from the Universidade de São Paulo (USP). Professor of the Graduate Program in Information Science at the Faculty of Information Science at the Universidade de Brasília (UnB), Brasília, Brazil.

Rafaella Monterei, University of Brasília

Master's student in Information Science at the Universidade de Brasília (UnB). Judicial analyst specialized support in Librarianship of the Superior Court of Justice. Brasília, Brazil.

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Published

2023-07-15

How to Cite

Martins, D. L., & Monterei, R. (2023). Machine learning and Information Science: contributions to a research and teaching agenda in the brazilian Information Science. Informação & Informação, 27(3), 751–774. https://doi.org/10.5433/1981-8920.2022v27n3p751