Browse/search for people

Publication - Dr Tijl De Bie

    Ordinal Label Proportions


    Poyiadzi, R, Santos-Rodriguez, R & De Bie, T, 2019, ‘Ordinal Label Proportions’. in: Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim, Michele Berlingerio (eds) Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. Springer, Cham, pp. 306-321


    In Machine Learning, it is common to distinguish different degrees of supervision, ranging from fully supervised to completely unsupervised scenarios. However, lying in between those, the Learning from Label Proportions (LLP) setting [19] assumes the training data is provided in the form of bags, and the only supervision comes through the proportion of each class in each bag. In this paper, we present a novel version of the LLP paradigm where the relationship among the classes is ordinal. While this is a highly relevant scenario (e.g. customer surveys where the results can be divided into various degrees of satisfaction), it is as yet unexplored in the literature. We refer to this setting as Ordinal Label Proportions (OLP). We formally define the scenario and introduce an efficient algorithm to tackle it. We test our algorithm on synthetic and benchmark datasets. Additionally, we present a case study examining a dataset gathered from the Research Excellence Framework that assesses the quality of research in the United Kingdom

    Full details in the University publications repository