Self-training in significance space of support vectors for imbalanced biomedical event data

BMC Bioinformatics

Research

Tsendsuren Munkhdalai¹  Keun Ho Ryu¹  Oyun-Erdene Namsrai² 

[1] Database/Bioinformatics Laboratory, Chungbuk National University, Cheongju, South Korea;School of Information Technology, National University of Mongolia, Ulaanbaatar, Mongolia;

关键词: Support Vector Machine;    Support Vector Machine Classifier;    Support Vector Machine Model;    Unlabeled Data;    Event Extraction;   

DOI  :  10.1186/1471-2105-16-S7-S6

来源: Springer

【 摘 要 】

BackgroundPairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. However, there are two critical issues that are seldom dealt with by existing systems. First, an annotated corpus for training a prediction model is highly imbalanced. Second, supervised models trained on only a single annotated corpus can limit system performance. Fortunately, there is a large pool of unlabeled data containing much of the domain background that one can exploit.ResultsIn this study, we develop a new semi-supervised learning method to address the issues outlined above. The proposed algorithm efficiently exploits the unlabeled data to leverage system performance. We furthermore extend our algorithm to a two-phase learning framework. The first phase balances the training data for initial model induction. The second phase incorporates domain knowledge into the event extraction model. The effectiveness of our method is evaluated on the Genia event extraction corpus and a PubMed document pool. Our method can identify a small subset of the majority class, which is sufficient for building a well-generalized prediction model. It outperforms the traditional self-training algorithm in terms of f-measure. Our model, based on the training data and the unlabeled data pool, achieves comparable performance to the state-of-the-art systems that are trained on a larger annotated set consisting of training and evaluation data.

【 授权许可】

Unknown   
© Munkhdalai et al.; licensee BioMed Central Ltd. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.