| DOI | Resolve DOI: https://doi.org/10.1109/ICMLA.2015.58 |
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| Author | Search for: Bellinger, Colin; Search for: Japkowicz, Nathalie; Search for: Drummond, Christopher1 |
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| Affiliation | - National Research Council of Canada. Information and Communication Technologies
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| Format | Text, Article |
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| Conference | 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 9-11 December 2015, Miami, FL, USA |
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| Subject | Radioactive waste; Gamma-ray spectra; Synthetic oversampling; Autoencoders; Class imbalance |
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| Abstract | 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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| Publication date | 2015-12 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23000396 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 603adb86-bdee-4a71-80ec-7b55f87df7f8 |
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| Record created | 2016-07-13 |
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| Record modified | 2020-04-22 |
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