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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
Creator	Jmaiel, Mohamed Ammar, Bellaaj, Hatem Hamam, Habib Khadhraoui, Mayara Mehdi, Ben
source	PMC
abstract	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.
has issue date	2020-05-31(xsd:dateTime)
bibo:doi	10.1007/978-3-030-51517-1_31
has license	cc-by
sha1sum (hex)	5cfde2fef704af485f942a09d16e569fd154c2a7
schema:url	https://doi.org/10.1007/978-3-030-51517-1_31
resource representing a document's title	Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach
has PubMed Central identifier	PMC7313272
schema:publication	The Impact of Digital Technologies on Public Health in Developed and Developing Countries
resource representing a document's body	covid:5cfde2fef704af485f942a09d16e569fd154c2a7#body_text
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