Evolutionary classifier ensembles for semi-supervised learning

Ensemble learning plays an important role in pattern recognition. It combines multiple generated models to solve learning problems, such as classification, regression and feature selection. Existing ensemble methods combine classifiers which are generated and run in parallel. In this paper, a novel ensemble learning approach for semi-supervised learning is proposed. Different from existing ensemble methods, classifiers used for prediction are not generated in parallel. Instead, they are generated evolutionarily in the process of iterative semi-supervised learning. As we know, in the iterative semi-supervised learning the labeled set is increased gradually, based on which multiple classifiers would be generated. This evolutionary ensemble tries to combine the classifiers generated evolutionarily in the process of semi-supervised learning to tackle the learning problems. Compared to traditional semi-supervised learning, the proposed method can generate multiple classifiers with no added training time. Some classifier combining rules are also evaluated in this paper. Moreover, the single-view scenario is generalized to the scenario of multiple-view for a wide application. In order to assess the proposed method, four experiments including single-view and multiple-view problems are implemented. The empirical results indicate that our proposed method can significantly improve the accuracy and reliability of semi-supervised learning.