Possibilistic estimation of distributions to leverage sparse data in machine learning

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Tettamanzi, Andrea G.B. and Emsellem, David and da Costa Pereira, Célia and Venerandi, Alessandro and Fusco, Giovanni; Lesot, Marie-Jeanne and Vieira, Susana and Reformat, Marek Z. and Carvalho, João Paulo and Wilbik, Anna and Bouchon-Meunier, Bernadette and Yager, Ronald R., eds. (2020) Possibilistic estimation of distributions to leverage sparse data in machine learning. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Communications in Computer and Information Science . Springer, PRT, pp. 431-444. ISBN 9783030501457 (https://doi.org/10.1007/978-3-030-50146-4_32)

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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.

ORCID iDs

Tettamanzi, Andrea G.B., Emsellem, David, da Costa Pereira, Célia, Venerandi, Alessandro ORCID logoORCID: https://orcid.org/0000-0003-4887-0120 and Fusco, Giovanni; Lesot, Marie-Jeanne, Vieira, Susana, Reformat, Marek Z., Carvalho, João Paulo, Wilbik, Anna, Bouchon-Meunier, Bernadette and Yager, Ronald R.
CORE (COnnecting REpositories)