I have a multi-class and multi-label classification problem, i.e.: each sample can have more than one label associated to it and there is a total number of M
possible labels.
e.g.:
y[0] = [0]
y[1] = [0, 1]
y[2] = [1, 4, 3, 0]
y[3] = [0, 1]
- ...
y[100] = [1, 0, 3]
Counting the number of occurrences of each label, I can see that some labels are way more frequent than others. In the example above, for instance, 0
appears more often than 1
, 3
and 4
.
I can't figure out a smart (over-)sampling strategy to have a dataset where each label appears approximately the same number of times.
Any papers/idea on that?