Título A Comparison of Multi-Label Text Classification Models in Research Articles Labeled with Sustainable Development Goals
Autores Morales-Hernández R.C. , Juagüey J.G. , BECERRA ALONSO, DAVID
Publicación externa No
Medio IEEE Access
Alcance Article
Naturaleza Científica
Cuartil JCR 2
Cuartil SJR 1
Impacto JCR 3.90000
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142806421&doi=10.1109%2fACCESS.2022.3223094&partnerID=40&md5=6417a19a79a472c62d66258769625132
Fecha de publicacion 17/11/2022
ISI 000912625000001
Scopus Id 2-s2.0-85142806421
DOI 10.1109/ACCESS.2022.3223094
Abstract 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. © 2013 IEEE.
Palabras clave Classification (of information); Job analysis; Learning algorithms; Learning systems; Natural language processing systems; Planning; Text processing; Trees (mathematics); Classification algorithm; Cla
Miembros de la Universidad Loyola

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