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Journal article Open Access Scopus Source The journal / series is indexed in Scopus (SCImago list). The article itself has not been verified. 12 Citations Quartil Q2

A Novel Multi-View Ensemble Learning Architecture to Improve the Structured Text Classification

Carlos A. Gonçalves , Adrián Seara Vieira , Célia Talma Gonçalves , Rui Camacho , Eva Lorenzo Iglesias , Lourdes Borrajo Diz
ISLA — Instituto Politécnico de Gestão e Tecnologia
Information, Volume 13, Issue 6, 01 06 2022, pp. 283–283
DOI: 10.3390/info13060283

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Abstract
Multi-view ensemble learning exploits the information of data views. To test its efficiency for full text classification, a technique has been implemented where the views correspond to the document sections. For classification and prediction, we use a stacking generalization based on the idea that different learning algorithms provide complementary explanations of the data. The present study implements the stacking approach using support vector machine algorithms as the baseline and a C4.5 implementation as the meta-learner. Views are created with OHSUMED biomedical full text documents. Experimental results lead to the sustained conclusion that the application of multi-view techniques to full texts significantly improves the task of text classification, providing a significant contribution for the biomedical text mining research. We also have evidence to conclude that enriched datasets with text from certain sections are better than using only titles and abstracts.

Keywords

#support vector machine #biomedical text mining #OHSUMED #data enrichment #Computer science #Exploit #generalization #Task (project management) #Ensemble learning #Stacking #Artificial Intelligence #Machine Learning

Bibliographic Details

Publication Type Journal article
Publication Year 2022
Journal / Conference / Work Information
ISSN 2078-2489
Volume / Issue Vol. 13 (6)
Pagination pp. 283–283
Quartile (SCImago) Q2
Scopus Indexing Indexed source (via SCImago)

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