Author: Khadhraoui, Mayara; Bellaaj, Hatem; Ben Ammar, Mehdi; Hamam, Habib; Jmaiel, Mohamed
Title: Machine Learning Classification Models with SPD/ED Dataset: Comparative Study of Abstract Versus Full Article Approach Cord-id: iqzx950c Document date: 2020_5_31
ID: iqzx950c
Snippet: 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 a
Document: 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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