Ordinal Label Proportions

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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
Original languageEnglish
Title of host publication Machine Learning and Knowledge Discovery in Databases
Subtitle of host publicationEuropean Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
EditorsFrancesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim, Michele Berlingerio
PublisherSpringer, Cham
Number of pages16
ISBN (Electronic)9783030109257
ISBN (Print)9783030109240
Publication statusPublished - 18 Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11051 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Structured keywords

  • Digital Health


  • Discriminant learning
  • Label Proportions
  • Ordinal classification

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