Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach

In response to the researchers need in the bio-medical domain, we opted for automating the bibliographic research stage. In this context, several classification models of supervised machine learning are used. Namely the SVM, Random Forest, Decision Tree, KNN, and Gradient Boosting. In this paper, we...

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Autores principales: Khadhraoui, Mayara, Bellaaj, Hatem, Ben Ammar, Mehdi, Hamam, Habib, Jmaiel, Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313272/
http://dx.doi.org/10.1007/978-3-030-51517-1_31
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author Khadhraoui, Mayara
Bellaaj, Hatem
Ben Ammar, Mehdi
Hamam, Habib
Jmaiel, Mohamed
author_facet Khadhraoui, Mayara
Bellaaj, Hatem
Ben Ammar, Mehdi
Hamam, Habib
Jmaiel, Mohamed
author_sort Khadhraoui, Mayara
collection PubMed
description In response to the researchers need in the bio-medical domain, we opted for automating the bibliographic research stage. In this context, several classification models of supervised machine learning are used. Namely the SVM, Random Forest, Decision Tree, KNN, and Gradient Boosting. In this paper, we conduct a comparative study between experimental results of full article classification and abstract classification approaches. Furthermore, we evaluate our results by using evaluation metrics such as accuracy, precision, recall and F1-score. We observe that the abstract approach outperforms the full article approach in terms of learning time and efficiency.
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spelling pubmed-73132722020-06-24 Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach Khadhraoui, Mayara Bellaaj, Hatem Ben Ammar, Mehdi Hamam, Habib Jmaiel, Mohamed The Impact of Digital Technologies on Public Health in Developed and Developing Countries Article In response to the researchers need in the bio-medical domain, we opted for automating the bibliographic research stage. In this context, several classification models of supervised machine learning are used. Namely the SVM, Random Forest, Decision Tree, KNN, and Gradient Boosting. In this paper, we conduct a comparative study between experimental results of full article classification and abstract classification approaches. Furthermore, we evaluate our results by using evaluation metrics such as accuracy, precision, recall and F1-score. We observe that the abstract approach outperforms the full article approach in terms of learning time and efficiency. 2020-05-31 /pmc/articles/PMC7313272/ http://dx.doi.org/10.1007/978-3-030-51517-1_31 Text en © The Author(s) 2020 Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
spellingShingle Article
Khadhraoui, Mayara
Bellaaj, Hatem
Ben Ammar, Mehdi
Hamam, Habib
Jmaiel, Mohamed
Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title_full Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title_fullStr Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title_full_unstemmed Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title_short Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
title_sort machine learning classification models with spd/ed dataset: comparative study of abstract versus full article approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7313272/
http://dx.doi.org/10.1007/978-3-030-51517-1_31
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