Possibilistic estimation of distributions to leverage sparse data in machine learning

Andrea G.B. Tettamanzi; David Emsellem; Célia da Costa Pereira; Alessandro Venerandi; Giovanni Fusco

doi:10.1007/978-3-030-50146-4_32

Possibilistic estimation of distributions to leverage sparse data in machine learning

Andrea G.B. Tettamanzi, David Emsellem, Célia da Costa Pereira, Alessandro Venerandi, Giovanni Fusco

Architecture

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

Abstract

Prompted by an application in the area of human geography using machine learning to study housing market valuation based on the urban form, we propose a method based on possibility theory to deal with sparse data, which can be combined with any machine learning method to approach weakly supervised learning problems. More specifically, the solution we propose constructs a possibilistic loss function to account for an uncertain supervisory signal. Although the proposal is illustrated on a specific application, its basic principles are general. The proposed method is then empirically validated on real-world data.

Original language	English
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
Editors	Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager
Place of Publication	Cham, Switzerland
Publisher	Springer
Pages	431-444
Number of pages	14
ISBN (Print)	9783030501457
DOIs	https://doi.org/10.1007/978-3-030-50146-4_32
Publication status	Published - 19 Jun 2020
Event	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 - Lisbon, Portugal Duration: 15 Jun 2020 → 19 Jun 2020 https://ipmu2020.inesc-id.pt/

Publication series

Name	Communications in Computer and Information Science
Volume	1237 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
Country	Portugal
City	Lisbon
Period	15/06/20 → 19/06/20
Internet address	https://ipmu2020.inesc-id.pt/

Keywords

machine learning
possibility theory
weakly supervised learning

Access to Document

10.1007/978-3-030-50146-4_32

Tettamanzi-etal-IPMU-2020-Possibilistic-estimation-of-distributions-to-leverage-sparse-data-in-machine-learningAccepted author manuscript, 666 KB

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Tettamanzi, A. G. B., Emsellem, D., da Costa Pereira, C., Venerandi, A., & Fusco, G. (2020). Possibilistic estimation of distributions to leverage sparse data in machine learning. In M-J. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings (pp. 431-444). (Communications in Computer and Information Science; Vol. 1237 CCIS). Springer. https://doi.org/10.1007/978-3-030-50146-4_32

Tettamanzi, Andrea G.B. ; Emsellem, David ; da Costa Pereira, Célia ; Venerandi, Alessandro ; Fusco, Giovanni. / Possibilistic estimation of distributions to leverage sparse data in machine learning. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. editor / Marie-Jeanne Lesot ; Susana Vieira ; Marek Z. Reformat ; João Paulo Carvalho ; Anna Wilbik ; Bernadette Bouchon-Meunier ; Ronald R. Yager. Cham, Switzerland : Springer, 2020. pp. 431-444 (Communications in Computer and Information Science).

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title = "Possibilistic estimation of distributions to leverage sparse data in machine learning",

abstract = "Prompted by an application in the area of human geography using machine learning to study housing market valuation based on the urban form, we propose a method based on possibility theory to deal with sparse data, which can be combined with any machine learning method to approach weakly supervised learning problems. More specifically, the solution we propose constructs a possibilistic loss function to account for an uncertain supervisory signal. Although the proposal is illustrated on a specific application, its basic principles are general. The proposed method is then empirically validated on real-world data.",

keywords = "machine learning, possibility theory, weakly supervised learning",

author = "Tettamanzi, {Andrea G.B.} and David Emsellem and {da Costa Pereira}, C{\'e}lia and Alessandro Venerandi and Giovanni Fusco",

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booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings",

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Tettamanzi, AGB, Emsellem, D, da Costa Pereira, C, Venerandi, A & Fusco, G 2020, Possibilistic estimation of distributions to leverage sparse data in machine learning. in M-J Lesot, S Vieira, MZ Reformat, JP Carvalho, A Wilbik, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Communications in Computer and Information Science, vol. 1237 CCIS, Springer, Cham, Switzerland, pp. 431-444, 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, Lisbon, Portugal, 15/06/20. https://doi.org/10.1007/978-3-030-50146-4_32

Possibilistic estimation of distributions to leverage sparse data in machine learning. / Tettamanzi, Andrea G.B.; Emsellem, David; da Costa Pereira, Célia; Venerandi, Alessandro; Fusco, Giovanni.

Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. ed. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. Cham, Switzerland : Springer, 2020. p. 431-444 (Communications in Computer and Information Science; Vol. 1237 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Possibilistic estimation of distributions to leverage sparse data in machine learning

AU - Tettamanzi, Andrea G.B.

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AU - Fusco, Giovanni

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KW - possibility theory

KW - weakly supervised learning

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A2 - Reformat, Marek Z.

A2 - Carvalho, João Paulo

A2 - Wilbik, Anna

A2 - Bouchon-Meunier, Bernadette

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CY - Cham, Switzerland

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Y2 - 15 June 2020 through 19 June 2020

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Tettamanzi AGB, Emsellem D, da Costa Pereira C, Venerandi A, Fusco G. Possibilistic estimation of distributions to leverage sparse data in machine learning. In Lesot M-J, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Cham, Switzerland: Springer. 2020. p. 431-444. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-030-50146-4_32

Possibilistic estimation of distributions to leverage sparse data in machine learning

Abstract

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Keywords

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