Underrepresented class data
Hello all,
This topic might have been discussed in some earlier posts, in which case I apologize for the repetition.
One of the characteristics I repeatedly see in many data sets is that some important class in a multiclass problem is underrepresented. In other words, the data set doesn't have too many instances of points belonging to a particular class.
From Prof. Mostafa's lectures, I understand that there is an underlying probability distribution P that acts on the input space X to draw the samples from it, but this distribution is only a mathematical requisite for the Hoeffding and VC framework. If I understand correctly, it is not our objective to find or learn about this distribution in a typical machine learning problem. And yet, this distribution can cause the kind of underrepresentation scenario I described above.
My question is: does this underrepresentation have the potential to influence generalization? I feel that it must, but am not sure how to quantize it. What are the ways to overcome this problem, given a data set one can't change? Should one just wait for the data set to get equitable class representation over time? In fact, is the act of checking class representation an act of snooping in the first place?
I would appreciate any pointers or references on this matter.
Thank you.
