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Graph Classification Problem Input Data Size: (1280,32,16) --> 32 EEG channels , 16 features for each channel Labels Size : (1280) -> 2 classes

I want to classify 1280 data with features size : (1280,32,16) through a graph convolution neural network but on the validation stage, I didn't get a good f1-score because of classes imbalance. Is there any way to use something like ADASYN (on machine learning similar problems) but for graph type data?

Peter Lion
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  • Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. – Community Dec 16 '22 at 07:12

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