In this survey, we propose a new way to represent the spectrum of semi-supervised classifi- cation algorithms. We attempt to group them in a clear, future-proof way, allowing researchers and practitioners alike to gain insight into the way semi-supervised learning methods relate to each other, to existing supervised learning methods, and to the semi-supervised learning assumptions. The taxonomy is visualized in Fig. 3. At the highest level, it distinguishes between inductive and transductive methods, which give rise to distinct optimization procedures: the former attempt to find a classification model, whereas the latter are solely concerned with obtaining label predictions for the given unlabelled data points. At the second level, it considers the way the semi-supervised learning methods incorporate unlabelled data. This distinction gives rise to three distinct classes of inductive methods, each of which is related to supervised classifiers in a different way.