Data Editing for Semi-Supervised Co-Forest by the Local Cut Edge Weight Statistic Graph - Settouti, Nesma; Bechar, Mohammed El Amine; Daho, Mostafa El Habib; Chikh, Mohammed Amine (Transactions on Engineering and Computing Sciences, 2017)
In order to address the large amount of unlabeled training data problem, many semi-supervised algorithms have been proposed. The training data in semi-supervised learning may contain much noise due to the insufficient number of labeled data in training set. Such noise may snowball themselves in the following learning process and thus hurt the generalization ability of the final hypothesis. If such noise could be identified and removed by some strategy, the performance of the semi-supervised algorithms should be improved. However, such useful techniques of identifying and removing noise have been seldom explored in existing semi-supervised algorithms. In this paper, we use the semi-supervised ensemble method “Co-forest” with data editing (we call it CEWS-Co-forest) to improve sparsely labeled medical dataset. The cut edges weight statistic data editing technique is used to actively identify possibly mislabeled examples in the newly-labeled data throughout the co-labeling iterations in Co-forest. The fusion of semi-supervised ensemble method with data editing makes CEWS-co-Forest more robust to the sparsity and the distribution bias of the training data. It further simplifies the design of semi-supervised learning which makes CEWS-co-forest more efficient. An experimental study on several medical data sets shows encouraging results compared with state-of-the-art methods.