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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.
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https://hal.archives-ouvertes.fr/hal-02885825
Contributor : Andrea G. B. Tettamanzi <>
Submitted on : Wednesday, July 1, 2020 - 9:22:46 AM
Last modification on : Wednesday, June 16, 2021 - 12:38:02 PM
Long-term archiving on: : Wednesday, September 23, 2020 - 2:24:42 PM

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Andrea G. B. Tettamanzi, David Emsellem, Célia da Costa Pereira, Alessandro Venerandi, Giovanni Fusco. Possibilistic Estimation of Distributions to Leverage Sparse Data in Machine Learning. Marie-Jeanne Lesot; Susana M. Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020., Springer, pp.431-444, 2020, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Lisbon, Portugal, June 15-19, 2020, Proceedings, Part I, 978-3-030-50145-7. ⟨10.1007/978-3-030-50146-4_32⟩. ⟨hal-02885825⟩

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