Aprendizaje automático:
un análisis bibliométrico
DOI:
https://doi.org/10.5585/2023.24056Palabras clave:
machine learning, análisis de Big Data, análisis bibliométrico, predicción.Resumen
Objetivo: Presentar un panorama de artículos científicos publicados en los últimos diez años sobre el tema de aprendizaje automático (ML en Inglés), con énfasis en algoritmos predictivos.
Método/enfoque: Análisis bibliométrico, con apoyo del protocolo PRISMA, para evaluar autores, universidades y países, en cuanto a productividad, citaciones bibliográficas y enfoques en el tema, con una muestra de 773 artículos de las bases de datos Scopus y Web of Science, del 2013 a mayo/2023.
Originalidad/valor: Existe una ausencia de estudios en la literatura que consoliden artículos relacionados con ML y Big Data. La investigación contribuye a cubrir este vacío, favoreciendo el diseño de futuras acciones e investigaciones.
Principales resultados: En el corpus bibliométrico de ML se identificaron: autores más citados con mayor número de publicaciones, países y universidades más productivos, revistas con mayor número de publicaciones y citaciones, áreas de conocimiento con mayor número de publicaciones y las más prestigiosas. artículos. En los temas y dominios de ML, se identificaron lo siguiente: principales co-ocurrencias de palabras clave, temas emergentes (agrupados en cinco grupos) y nubes de palabras por título y resumen. Los estudios sobre el impacto de la adquisición de datos y el análisis predictivo representan oportunidades para futuras investigaciones.
Contribuciones teóricas/metodológicas: El protocolo PRISMA permitió la identificación y análisis cuantitativos y cualitativos relevantes de artículos, consolidando el conocimiento científico sobre el tema.
Contribuciones sociales/gerenciales: Facilidad de comprensión de la madurez de la investigación sobre ML y Big Data por parte de directivos e investigadores de empresas, en cuanto a la viabilidad de inversiones para obtener ventajas competitivas con dichas tecnologías.
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Derechos de autor 2023 Emerson Martins, Napoleao Verardi Galegale
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