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 |
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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 | |
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 |
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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 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 15/06/20 → 19/06/20 |
Internet address |
Keywords
- machine learning
- possibility theory
- weakly supervised learning