Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates

20 Jun 2018Justus T. C. SchwabedalJohn C. SnyderAyse CakmakShamim NematiGari D. Clifford

Randomizing the Fourier-transform (FT) phases of temporal-spatial data generates surrogates that approximate examples from the data-generating distribution. We propose such FT surrogates as a novel tool to augment and analyze training of neural networks and explore the approach in the example of sleep-stage classification... (read more)

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