title: Learning Boolean queries for article quality filtering.
abstract: Prior research has shown that Support Vector Machine models have the ability to identify high quality content-specific articles in the domain of internal medicine. These models, though powerful, cannot be used in Boolean search engines nor can the content of the models be verified via human inspection. In this paper, we use decision trees combined with several feature selection methods to generate Boolean query filters for the same domain and task. The resulting trees are generated automatically and exhibit high performance. The trees are understandable, manageable, and able to be validated by humans. The subsequent Boolean queries are sensible and can be readily used as filters by Boolean search engines.
authors.affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA. ping.pong@vanderbilt.edu
authors.forename: Yin
authors.initials: Y
authors.lastname: Aphinyanaphongs
authors.forename: Constantin F
authors.initials: CF
authors.lastname: Aliferis
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D016247:Information Storage and Retrieval / Q000379:methods* / Q000592:standards; D046650:Medical Subject Headings; D010506:Periodicals as Topic; D039781:PubMed