Ali_Aghababaei:

So in your opinion, do you think that a more easy way to deal with data imbalance in multi-label classification is to calculate the loss with respect to the class weights


Yes, I believe it could be easier to experiment with a weighted loss.



 Ali_Aghababaei:

Also, is there any point in the post…


I don’t think that multiplying the weights directly will yield balanced samples (this should also be explained in the linked papers).



 Ali_Aghababaei:

One more question is that I cannot use sklearn.utils.class_weight.compute_class_weight to calculate the class weights since the targets are multi-label and in a one-hot coded format, can I?


Yes, I don’t think it’s possible using the standard “counting” method.