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[D] Techniques to sample unbalanced multi-label datasets?

I have a dataset with multi-label classification outputs. These are (fortunately) binary, so a typical output is basically a binary vector like [1, 0, 0, 1, 1]. The length of this vector is fixed.

The dataset is pretty biased towards all 0’s, and since it is a multi-label output (and not like a one-hot encoding), I’m not sure what is the best way to undersample or oversample the dataset for my training epochs, since traditional stratification cannot work here.

Edit: All kinds of suggestions are welcome, be it simple beginner methods or ICLR papers 😉

submitted by /u/parekhnish
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.