Load Balancing for Imbalanced Data Sets: Classifying Scientific Artefacts for Evidence Based Medicine

Abstract

Data skewness is a challenge encountered, in particular, when applying supervised machine learning approaches in various domains, such as in healthcare and biomedical information engineering. Evidence Based Medicine (EBM) is a clinical strategy for prescribing treatment based on current best evidence for individual patients. Clinicians need to query publication repositories in order to find the best evidence to support their decision-making processes. This sophisticated information is materialised in the form of scientific artefacts in scholarly publications and the automatic extraction of these artefacts is a technical challenge for current generic search engines. Many classification approaches have been proposed for identifying key scientific artefacts in EBM, however their performance is affected by the imbalanced characteristic of data in this domain. In this paper, we present four data balancing approaches applied in a binary ensemble classifier framework for classifying scientific artefacts in the EBM domain. Our balancing approaches improve the ensemble classifier’s F-score by up to 15% for classes of scientific artefacts with extremely low coverage in the domain. In addition, we propose a classifier selection method for choosing the best classifier based on the distributional feature of classes. The resulting classifiers show improved classification performances when compared to state of the art approaches.