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Chapitre D'ouvrage Année : 2020

Possibilistic Estimation of Distributions to Leverage Sparse Data in Machine Learning

Résumé

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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Dates et versions

hal-02885825 , version 1 (01-07-2020)

Identifiants

Citer

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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