Multi-view based unlabeled data selection using feature transformation methods for semiboost learning

Abstract

SemiBoost Mallapragada et al. (2009) is a boosting framework for semi-supervised learning, in which unlabeled data as well as labeled data both contribute to learning. Various strategies have been proposed in the literature to perform the task of selecting useful unlabeled data in SemiBoost. Recently, a multi-view based strategy was proposed in Le and Kim (2016), in which the feature set of the data is decomposed into subsets (i.e., multiple views) using a feature-decomposition method. In the decomposition process, the strategy inevitably results in some loss of information. To avoid this drawback, this paper considered feature-transformation methods, rather than using the decomposition method, to obtain the multiple views. More specifically, in the feature-transformation method, a number of views were obtained from the entire feature set using the same number of different mapping functions. After deriving the number of views of the data, each of the views was used for measuring corresponding confidences, for first evaluating examples to be selected. Then, all the confidence levels measured from the multiple views were combined as a weighted average for deriving a target confidence. The experimental results, which were obtained using support vector machines for well-known benchmark data, demonstrate that the proposed mechanism can compensate for the shortcomings of the traditional strategies. In addition, the results demonstrate that when the data is transformed appropriately into multiple views, the strategy can achieve further improvement in results in terms of classification accuracy.