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Ordinal Label Proportions

  • Rafael PoyiadziEmail author
  • Raúl Santos-Rodríguez
  • Tijl De Bie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11051)

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.

Keywords

Label Proportions Ordinal classification Discriminant learning 

Notes

Acknowledgements

The research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement no. 615517, from the FWO (project no. G091017N, G0F9816N), and from the European Union’s Horizon 2020 research and innovation programme and the FWO under the Marie Sklodowska-Curie Grant Agreement no. 665501. Additionally, this study was supported by EPSRC and MRC through the SPHERE IRC (EP/K031910/1) and CUBOID (MC/PC/16029) respectively.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rafael Poyiadzi
    • 1
    Email author
  • Raúl Santos-Rodríguez
    • 1
  • Tijl De Bie
    • 2
  1. 1.Department of Engineering MathematicsUniversity of BristolBristolUK
  2. 2.Department of Electronics and Information Systems, IDLabGhent UniversityGhentBelgium

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