DOI | Trouver le DOI : https://doi.org/10.1109/ICMLA.2015.58 |
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Auteur | Rechercher : Bellinger, Colin; Rechercher : Japkowicz, Nathalie; Rechercher : Drummond, Christopher1 |
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Affiliation | - Conseil national de recherches du Canada. Technologies de l'information et des communications
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Format | Texte, Article |
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Conférence | 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 9-11 December 2015, Miami, FL, USA |
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Sujet | Radioactive waste; Gamma-ray spectra; Synthetic oversampling; Autoencoders; Class imbalance |
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Résumé | Gamma-ray spectral classification requires the automatic identification of a large background class and a small minority class composed of instances that may pose a risk to humans and the environment. Accurate classification of such instances is required in a variety of domains, spanning event and port security to national monitoring for failures at industrial nuclear facilities. This work proposes a novel form of synthetic oversampling based on artificial neural network architecture and empirically demonstrates that it is superior to the state-of-the-art in synthetic oversampling on the target domain. In particular, we utilize gamma-ray spectral data collected for security purposes at the Vancouver 2010 winter Olympics and on a node of Health Canada's national monitoring networks. |
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Date de publication | 2015-12 |
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Maison d’édition | IEEE |
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Dans | |
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Langue | anglais |
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Publications évaluées par des pairs | Oui |
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Numéro NPARC | 23000396 |
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Exporter la notice | Exporter en format RIS |
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Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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Identificateur de l’enregistrement | 603adb86-bdee-4a71-80ec-7b55f87df7f8 |
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Enregistrement créé | 2016-07-13 |
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Enregistrement modifié | 2020-04-22 |
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