I am trying to do a Multiple Instance Learning for a binary classification problem, where each bag of instances has an associated label 0/1. However, the different bags have different numbers of instances. One solution is to take the minimum of all the instance numbers of the bag. For eg-
Bag1 - 20 instances, Bag2- 5 instances, Bag3 - 10 instances .... etc
I am taking the minimum i.e- 5 instances from all the bags. However, this technique discards all the other instances from other bags which might contribute to the training.
Is there any workaround/algorithm for MIL where variable instance numbers for bags could be handled?