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A Comparison of Multi-Label Text Classification Models in Research Articles Labeled With Sustainable Development Goals
Roberto Carlos Morales-Hernández
Joaquín Gutiérrez Jaguey
David Becerra-Alonso
Acceso Abierto
Atribución-NoComercial-SinDerivadas
DOI: 10.1109/ACCESS.2022.3223094
URL: https://ieeexplore.ieee.org/document/9954368
ISSN: 21693536
Classification algorithm, multi-label text classification, problem transformation method, scientific articles, sustainable development goals, text classification
"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."
Institute of Electrical and Electronics Engineers Inc.
2022
Artículo
IEEE Access
Inglés
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.
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