Abstract
Imbalanced data is a well-known common problem in many practical applications of machine learning and its effects on the performance of standard classifiers are remarkable. In this paper we investigate if the classification of Medline documents using MeSH controlled vocabulary poses additional challenges when dealing with class-imbalanced prediction. For this task, we evaluate the performance of Bayesian networks by using some available strategies to overcome the effect of class imbalance. Our results show both that Bayesian network classifiers are sensitive to class imbalance and existing techniques can improve their overall performance.
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Pavón, R., Laza, R., Reboiro-Jato, M., Fdez-Riverola, F. (2011). Assessing the Impact of Class-Imbalanced Data for Classifying Relevant/Irrelevant Medline Documents. In: Rocha, M.P., Rodríguez, J.M.C., Fdez-Riverola, F., Valencia, A. (eds) 5th International Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB 2011). Advances in Intelligent and Soft Computing, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19914-1_45
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DOI: https://doi.org/10.1007/978-3-642-19914-1_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19913-4
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