Abstract
A model designed for automatic prediction of diseases based on multimedia data collected in hospitals is introduced in this working notes paper. In order to perform the automatic diseases prediction efficiently, while using as few data as possible for training, we develop a two-stage learning strategy, which first performs the weighted discriminant embedding (WDE) to project the original data to a low-dimensional feature subspace and then utilizes the cost-sensitive nearest neighbor (CS-NN) method in the learned subspace for disease prediction. The proposed approach is evaluated on the MediaEval 2018 Medico Multimedia Task. Copyright held by the owner/author(s).
Original language | English |
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Title of host publication | Working Notes Proceedings of the MediaEval 2018 Workshop |
Editors | Martha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones |
Publisher | CEUR-WS |
Number of pages | 3 |
Publication status | Published - Oct 2018 |
Event | MediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France Duration: 29 Oct 2018 → 31 Oct 2018 https://ceur-ws.org/Vol-2283/ |
Publication series
Name | CEUR Workshop Proceedings |
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Publisher | CEUR-WS |
Volume | 2283 |
ISSN (Print) | 1613-0073 |
Conference
Conference | MediaEval 2018: Multimedia Benchmark Workshop |
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Country/Territory | France |
City | Sophia Antipolis |
Period | 29/10/18 → 31/10/18 |
Internet address |
Scopus Subject Areas
- General Computer Science