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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 781807, 11 pages
http://dx.doi.org/10.1155/2014/781807
Research Article

On Multilabel Classification Methods of Incompletely Labeled Biomedical Text Data

1Center for Pediatric Hematology, Oncology, and Immunology, Moscow 117997, Russia
2Moscow Institute of Physics and Technology, Moscow 117303, Russia
3The Biogerontology Research Foundation, Reading W1J 5NE, UK
4Chemistry Department, Moscow State University, Moscow 119991, Russia

Received 9 September 2013; Revised 8 December 2013; Accepted 12 December 2013; Published 23 January 2014

Academic Editor: Dejing Dou

Copyright © 2014 Anton Kolesov et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multilabel classification is often hindered by incompletely labeled training datasets; for some items of such dataset (or even for all of them) some labels may be omitted. In this case, we cannot know if any item is labeled fully and correctly. When we train a classifier directly on incompletely labeled dataset, it performs ineffectively. To overcome the problem, we added an extra step, training set modification, before training a classifier. In this paper, we try two algorithms for training set modification: weighted k-nearest neighbor (WkNN) and soft supervised learning (SoftSL). Both of these approaches are based on similarity measurements between data vectors. We performed the experiments on AgingPortfolio (text dataset) and then rechecked on the Yeast (nontext genetic data). We tried SVM and RF classifiers for the original datasets and then for the modified ones. For each dataset, our experiments demonstrated that both classification algorithms performed considerably better when preceded by the training set modification step.