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

• Tipo: Artigo em Revista Científica Internacional
• Data: 2022

Autores

Goncalves, CA (Autor) Outra; Vieira, AS (Autor) Outra; Goncalves, CT (Autor) Outra; Rui Camacho (Autor) FEUP; Iglesias, EL (Autor) Outra; Diz, LB (Autor) Outra

Revista

Título: Information (Switzerland)

Vol. 13

Página Final: 283

ISSN: 2078-2489

Editora: MDPI

Indexação

ISI Web of Knowledge - 4 Citações

Scopus

Outras Informações

ID Authenticus: P-00W-TTN

DOI: 10.3390/info13060283

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15