Classifier Ensemble for Biomedical Document Retrieval.
Manabu Torii, Hongfang Liu · 2007
Background Due to rich information embedded in published articles, literature review has become an important aspect of research activities in the biomedical domain. Machine Learning (ML) techniques have been explored to retrieve relevant articles from a large literature archive (i.e., classifying articles into relevant and irrelevant classes), and to accelerating the literature review process. Meanwhile, an ensemble classifier, a system that assigns classes based on the outputs of multiple classifiers, tends to be more robust and has better performance than each individual classifier. Ensemble classifiers are often composed of classifiers trained on different training sets (e.g., sampled data sets) or of those using different ML algorithms. In this paper, we propose a simple ensemble approach where an ensemble is composed of classifiers using different feature sets for an ML algorithm. We evaluated the approach using Support Vector Machine (SVM) on two publicly available collections of MEDLINE citations, the Post-translational modification (PTM) data sets and the Immune Epitope Database (IEDB) data sets, that resulted from biomedical database curation projects.