shartzog:

None


I would like to add my two cents, although I didn’t read all the discussion. If the labels of ‘None’ are known a prior; then you can predict ‘None’ as a form of Anomaly Detection based on the other classes.
Otherwise, I think it’s a good option to add a/the ‘None’ label if they are available at training time. By not doing so, what you are trying is the following:
Designed labels
 [1, 0, 0, 0]; [0, 1, 0, 0]; [0, 0, 1, 0]; [0, 0, 0, 1]
Then, the prediction of None is made via the Ad-hoc label [0, 0, 0, 0]. IMHO, having five classes (after including None); the model should output five logits. This is what the machine literature has found to work best.
Finally, it is a good idea to pay a special attention at the data balance;  as it is expect that samples with ‘None’ (presumably healthy subjects) to dominate such datasets.