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Ordinal Label Proportions

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

Standard

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 proceedingConference contribution

Harvard

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

APA

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

Vancouver

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)). https://doi.org/10.1007/978-3-030-10925-7_19

Author

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)).

Bibtex

@inproceedings{8202a29e1e15463ebed01dde260933a2,
title = "Ordinal Label Proportions",
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 = "Discriminant learning, Label Proportions, Ordinal classification",
author = "Rafael Poyiadzi and Raul Santos-Rodriguez and {De Bie}, Tijl",
year = "2019",
month = "1",
day = "18",
doi = "10.1007/978-3-030-10925-7_19",
language = "English",
isbn = "9783030109240",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "306--321",
editor = "Francesco Bonchi and Thomas G{\"a}rtner and Neil Hurley and Georgiana Ifrim and Michele Berlingerio",
booktitle = "Machine Learning and Knowledge Discovery in Databases",
address = "Switzerland",

}

RIS - suitable for import to EndNote

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

KW - Label Proportions

KW - Ordinal classification

UR - http://www.scopus.com/inward/record.url?scp=85061143252&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-10925-7_19

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

M3 - Conference contribution

SN - 9783030109240

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 306

EP - 321

BT - Machine Learning and Knowledge Discovery in Databases

A2 - Bonchi, Francesco

A2 - Gärtner, Thomas

A2 - Hurley, Neil

A2 - Ifrim, Georgiana

A2 - Berlingerio, Michele

PB - Springer, Cham

ER -