Weak supervision and other non-standard classification problems: A taxonomy

摘要

In recent years, different researchers in the machine learning community have presented new classification frameworks which go beyond the standard supervised classification in different aspects. Specifically, a wide spectrum of novel frameworks that use partially labeled data in the construction of classifiers has been studied. With the objective of drawing up a description of the state-of-the-art, three identifying characteristics of these novel frameworks have been considered: (1) the relationship between instances and labels of a problem, which may be beyond the one-instance one-label standard, (2) the possible provision of partial class information for the training examples, and (3) the possible provision of partial class information also for the examples in the prediction stage. These three ideas have been formulated as axes of a comprehensive taxonomy that organizes the state-of-the-art. The proposed organization allows us both to understand similarities/differences among the different classification problems already presented in the literature as well as to discover unexplored frameworks that might be seen as further challenges and research opportunities. A representative set of state-of-the-art problems has been used to illustrate the novel taxonomy and support the discussion. (C) 2015 Elsevier B.V. All rights reserved.