Hi all. This question is breaking my brain - can anyone help?
I’m working on a classification problem where I know for sure that 10% of the data is mislabeled. After a bit of training for example, the sample is labelled green, but it’s actually yellow, and the model predicts it as yellow. To the model, this looks like a wrong prediction, right? But I know it is likely to be correctly classified.
I would ideally want the gradient to pull the true positives more positive and pull the “false negatives” less strongly toward their incorrect label.
Does it make any sense at all to have negatives count for less in the loss function?
Is there a name for this idea, and how would I implement it?
Thanks for any clarifications!