Safe Multi-view Co-training for Semi-supervised Regression

Abstract

Co-training is a popular disagreement-based semi-supervised learning method. Learners of different views mutually select reliable unlabeled instances to augment the labeled dataset. Existing co-training style algorithms have cumbersome procedures for selecting confident instances. Furthermore, the pseudo-labels assigned to selected unlabeled instances are not always reliable. In this paper, we propose a safe co-training regression algorithm for multi-view scenarios with two characteristics. An instance selection strategy based on the consistency assumption aims to improve the efficiency of selecting confident unlabeled instances. This strategy makes full use of the information provided by a committee to measure the confidence of unlabeled instances. A safe labeling technique in an ensemble manner is introduced to improve the quality of pseudo-labels. The safe pseudo-labels not only integrate information provided by the committee, but also take into account the part of the receiver. The results over twenty datasets prove the superiority of the proposed algorithm against other state-of-the-art semi-supervised regression algorithms.