Título

A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals

Autor

Roberto Carlos Morales-Hernández

Joaquín Gutiérrez Jaguey

David Becerra-Alonso

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Referencia de publicación

doi: DOI: 10.1109/ACCESS.2022.3223094

URL/URL: https://ieeexplore.ieee.org/document/9954368

ISSN/ISSN: 21693536

Materias

Classification algorithm, multi-label text classification, problem transformation method, scientific articles, sustainable development goals, text classification - (AUTOR) INGENIERÍA Y TECNOLOGÍA - (CTI) CIENCIAS TECNOLÓGICAS - (CTI) TECNOLOGÍA DE LOS ORDENADORES - (CTI) LENGUAJES ALGORÍTMICOS - (CTI) LENGUAJES ALGORÍTMICOS - (CTI)

Resumen o descripción

"The classification of scientific articles aligned to Sustainable Development Goals is crucial for research institutions and universities when assessing their influence in these areas. Machine learning enables the implementation of massive text data classification tasks. The objective of this study is to apply Natural Language Processing techniques to articles from peer-reviewed journals to facilitate their classification according to the 17 Sustainable Development Goals of the 2030 Agenda. This article compares the performance of multi-label text classification models based on a proposed framework with datasets of different characteristics. The results show that the combination of Label Powerset (a transformation method) with Support Vector Machine (a classification algorithm) can achieve an accuracy of up to 87% for an imbalanced dataset, 83% for a dataset with the same number of instances per label, and even 91% for a multiclass dataset."

Editor

Institute of Electrical and Electronics Engineers Inc.

Fecha de publicación

2022

Tipo de publicación

Artículo

Versión de la publicación

Versión publicada

Recurso de información

http://cibnor.repositorioinstitucional.mx/jspui/handle/1001/2821

Formato

application/pdf

Fuente

IEEE Access

Idioma

Inglés

Sugerencia de citación

R. C. Morales-Hernández, J. G. Jagüey and D. Becerra-Alonso, "A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals," in IEEE Access, vol. 10, pp. 123534-123548, 2022, doi: 10.1109/ACCESS.2022.3223094.

Repositorio Orígen

Repositorio Institucional CIBNOR

Descargas

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