Discriminative Learning from Selective Recommendation and Its Application in AdaBoost

The integration of semi-supervised learning and ensemble learning has been a promising research area. It is a typical procedure that one learner recommends the pseudo-labeled instances with high predictive confidence to another, so that the training dataset is expanded. However, the new learner's demand on recommendation as well as the possibility of incorrect recommendation are neglected, which inevitably jeopardize the learning performance. To address these issues, this paper proposes the Discriminative Learning from Selective Recommendation (DLSR) method. On one hand, both reliability and informativeness of the pseudo-labeled instances are taken into account via selective recommendation. On the other hand, the potential in both correct and incorrect recommendation are formulated in discriminative learning. Based on DLSR, we further propose the selective semi-supervised AdaBoost. With both recommending and receiving learners engaged in ensemble model learning, the unlabeled instances are explored in a more effective way.