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The Scientific World Journal
Volume 2013, Article ID 875450, 9 pages
http://dx.doi.org/10.1155/2013/875450
Research Article

Iterative Nearest Neighborhood Oversampling in Semisupervised Learning from Imbalanced Data

School of Software, Dalian University of Technology, Dalian 116620, China

Received 12 May 2013; Accepted 4 June 2013

Academic Editors: P. Melin and J. Pavón

Copyright © 2013 Fengqi Li 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

Transductive graph-based semisupervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices. Those methods propagate label information of labeled samples to neighbors through their edges in order to get the predicted labels of unlabeled samples. Most popular semi-supervised learning approaches are sensitive to initial label distribution which happened in imbalanced labeled datasets. The class boundary will be severely skewed by the majority classes in an imbalanced classification. In this paper, we proposed a simple and effective approach to alleviate the unfavorable influence of imbalance problem by iteratively selecting a few unlabeled samples and adding them into the minority classes to form a balanced labeled dataset for the learning methods afterwards. The experiments on UCI datasets and MNIST handwritten digits dataset showed that the proposed approach outperforms other existing state-of-art methods.