title: Machine learning for biomedical literature triage.
abstract: This paper presents a machine learning system for supporting the first task of the biological literature manual curation process, called triage. We compare the performance of various classification models, by experimenting with dataset sampling factors and a set of features, as well as three different machine learning algorithms (Naive Bayes, Support Vector Machine and Logistic Model Trees). The results show that the most fitting model to handle the imbalanced datasets of the triage classification task is obtained by using domain relevant features, an under-sampling technique, and the Logistic Model Trees algorithm.
authors.affiliation: Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.
authors.forename: Hayda
authors.initials: H
authors.lastname: Almeida
authors.affiliation: Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada.
authors.forename: Marie-Jean
authors.initials: MJ
authors.lastname: Meurs
authors.affiliation: Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada.
authors.forename: Leila
authors.initials: L
authors.lastname: Kosseim
authors.affiliation: Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, Canada; Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada.
authors.forename: Greg
authors.initials: G
authors.lastname: Butler
authors.affiliation: Centre for Structural and Functional Genomics, Concordia University, Montreal, QC, Canada.
authors.forename: Adrian
authors.initials: A
authors.lastname: Tsang
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D016206:Databases, Bibliographic; D003663:Decision Trees; D008490:Medical Informatics / Q000379:methods*; D008962:Models, Theoretical; D060388:Support Vector Machine