| Title |
A Comparison of Multi-Label Text Classification Models in Research Articles Labeled with Sustainable Development Goals |
| Authors |
Morales-Hernández R.C. , Juagüey J.G. , BECERRA ALONSO, DAVID |
| External publication |
No |
| Means |
IEEE Access |
| Scope |
Article |
| Nature |
Científica |
| JCR Quartile |
2 |
| SJR Quartile |
1 |
| JCR Impact |
3.90000 |
| SJR Impact |
0.92600 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142806421&doi=10.1109%2fACCESS.2022.3223094&partnerID=40&md5=6417a19a79a472c62d66258769625132 |
| Publication date |
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. |
| Keywords |
Classification (of information); Job analysis; Learning algorithms; Learning systems; Natural language processing systems; Planning; Text processing; Trees (mathematics); Classification algorithm; Cla |
| Universidad Loyola members |
|
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