National Research Council of Canada. Information and Communication Technologies
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), 9-11 December 2015, Miami, FL, USA
Radioactive waste; Gamma-ray spectra; Synthetic oversampling; Autoencoders; Class imbalance
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.
2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA): 948–953.