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Ordinal Label Proportions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Publisher or commissioning bodySpringer, Cham
Pages306-321
Number of pages16
ISBN (Electronic)9783030109257
ISBN (Print)9783030109240
DOIs
DateAccepted/In press - 15 Jun 2018
DatePublished (current) - 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

Abstract

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

    Research areas

  • Discriminant learning, Label Proportions, Ordinal classification

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Springer at https://link.springer.com/chapter/10.1007%2F978-3-030-10925-7_19 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 439 KB, PDF document

    Embargo ends: 18/01/20

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DOI

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