Title

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

Author

Roberto Carlos Morales-Hernández

Joaquín Gutiérrez Jaguey

David Becerra-Alonso

Access level

Open Access

Publication reference

doi: DOI: 10.1109/ACCESS.2022.3223094

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

ISSN/ISSN: 21693536

Summary or description

"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."

Publisher

Institute of Electrical and Electronics Engineers Inc.

Publish date

2022

Publication type

Article

Publication version

Published Version

Format

application/pdf

Source

IEEE Access

Language

English

Citation suggestion

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.

Source repository

Repositorio Institucional CIBNOR

Downloads

12

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