A hybrid safe semi-supervised learning method

Abstract

Within the past few years, Safe Semi-Supervised Learning (S3L) has become a hot topic in the machine learning field and many related S3L methods have been proposed to safely exploit the unlabeled information. However, these methods only considered the risk from a single level, such as the instance or model level. They can not reduce the adverse effects of both the risky unlabeled instances and inappropriate learning models. Therefore, it is important to investigate a novel effective S3L method. In this paper, we present a hybrid S3L method which can inherit the merits of both the instance-level and model-level approaches. In our algorithm, multiple Graph-based SSL (GSSL) classifiers are firstly trained and used to predict the unlabeled instances. The risk degrees of the unlabeled instances and the qualities of the constructed graphs are then estimated through the predictions of multiple GSSL classifiers. Finally, we build two regularization terms to constrain the predictions of the unlabeled instances and adaptively select the graphs with high qualities. These regularization terms aim at reducing the negative effect of both the risky unlabeled instances and inappropriate learning models with low-quality graphs. Experimental results on different real-world datasets verify the effectiveness of our algorithm by comparisons to the state-of-the-art Supervised Learning (SL), SSL and S3L methods. In conclusion, our algorithm can not only enrich the research of S3L, but enlarge the practical scope of SSL in the expert and intelligent systems to a certain extent.