Ordinal Label Proportions

Rafael Poyiadzi; Raul Santos-Rodriguez; Tijl De Bie

doi:10.1007/978-3-030-10925-7_19

Ordinal Label Proportions

Rafael Poyiadzi, Raul Santos-Rodriguez, Tijl De Bie

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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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

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I
Editors	Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim, Michele Berlingerio
Publisher	Springer, Cham
Pages	306-321
Number of pages	16
ISBN (Electronic)	9783030109257
ISBN (Print)	9783030109240
DOIs	https://doi.org/10.1007/978-3-030-10925-7_19
Publication status	Published - 18 Jan 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11051 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Research Groups and Themes

Digital Health
SPHERE

Keywords

Discriminant learning
Label Proportions
Ordinal classification

Access to Document

10.1007/978-3-030-10925-7_19

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Poyiadzi, R., Santos-Rodriguez, R., & De Bie, T. (2019). Ordinal Label Proportions. In F. Bonchi, T. Gärtner, N. Hurley, G. Ifrim, & M. Berlingerio (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I (pp. 306-321). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11051 LNAI). Springer, Cham. https://doi.org/10.1007/978-3-030-10925-7_19

Poyiadzi, Rafael ; Santos-Rodriguez, Raul ; De Bie, Tijl. / Ordinal Label Proportions. Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. editor / Francesco Bonchi ; Thomas Gärtner ; Neil Hurley ; Georgiana Ifrim ; Michele Berlingerio. Springer, Cham, 2019. pp. 306-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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",

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Poyiadzi, R, Santos-Rodriguez, R & De Bie, T 2019, Ordinal Label Proportions. in F Bonchi, T Gärtner, N Hurley, G Ifrim & M Berlingerio (eds), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11051 LNAI, Springer, Cham, pp. 306-321. https://doi.org/10.1007/978-3-030-10925-7_19

Ordinal Label Proportions. / Poyiadzi, Rafael; Santos-Rodriguez, Raul; De Bie, Tijl.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. ed. / Francesco Bonchi; Thomas Gärtner; Neil Hurley; Georgiana Ifrim; Michele Berlingerio. Springer, Cham, 2019. p. 306-321 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11051 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Ordinal Label Proportions

AU - Poyiadzi, Rafael

AU - Santos-Rodriguez, Raul

AU - De Bie, Tijl

PY - 2019/1/18

Y1 - 2019/1/18

N2 - 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

AB - 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

KW - Discriminant learning

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PB - Springer, Cham

ER -

Poyiadzi R, Santos-Rodriguez R, De Bie T. Ordinal Label Proportions. In Bonchi F, Gärtner T, Hurley N, Ifrim G, Berlingerio M, editors, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I. Springer, Cham. 2019. p. 306-321. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-10925-7_19

Ordinal Label Proportions

Abstract

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Momentum Award - The use of home sensors in the early diagnosis and monitoring of Dementia (CUBOId)

SPHERE (EPSRC IRC)

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Ordinal Label Proportions

Abstract

Publication series

Research Groups and Themes

Keywords

Access to Document

Handle.net

Other files and links

Fingerprint

Projects

Momentum Award - The use of home sensors in the early diagnosis and monitoring of Dementia (CUBOId)

SPHERE (EPSRC IRC)

Cite this