  Transductive graph-based semi-supervised 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 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.
