A SEMI-SUPERVISED ENSEMBLE LEARNING ALGORITHM

The success of ensemble learning usually depends on available labeled data. We present a new and more general co-training style framework, ensemble cotraining, to combine ensemble learning with semi-supervised learning. A new classifier is generated using both labeled data and the most confident newly-predicted data, and added into the ensemble at each round. Finally, the most credible classifiers are selected for the final prediction. Furthermore, we provide a theoretical analysis for the learnable ability of cotraining style algorithms in the presence of both classification noise and distribution noise. We demonstrate our algorithm on six text datasets, and the results show that the ensemble co-training performs better than other state-of-the-art algorithms in practice.