I have a data set that has some holes in it. I was wondering if I could add two columns for each feature representing
this feature has data and
this feature doesn't have data for each of my features. Would a CNN be able to take advantage of this or should I just use another method to approximate the result.
Note The reason why I'm looking for an alternative is that I have a very small dataset and I assume extrapolating would insert to great of a bias on whatever method I use determine the missing value.
0.0,0.3,0.2 0.4,Nan,0.2 0.2,0.6,Nan Nan,0.1,0.5 0.0,0.2,0.0 0.9,Nan,Nan
Would be converted to
0.0,1,0, 0.3,1,0, 0.2,1,0 0.4,1,0, 0.0,0,1, 0.2,1,0 0.2,1,0, 0.6,1,0, 0.0,0,1 0.0,0,1, 0.1,1,0, 0.5,1,0 0.0,1,0, 0.2,1,0, 0.0,1,0 0.9,1,0, 0.0,0,1, 0.0,0,1
Since my dataset is already relatively small I don't particularly want to remove entries. This would reduce my data by roughly a third.
If this is a 'foul ball out of left field' (makes no sense and would cause more errors) how would you deal with a small data set with a decent amount of holes.
EDIT: As per @zachdj suggestion in comments
Would be instead converted to this data set which is much smaller.
0.0,1, 0.3,1, 0.2,1 0.4,1, 0.0,0, 0.2,1 0.2,1, 0.6,1, 0.0,0 0.0,0, 0.1,1, 0.5,1 0.0,1, 0.2,1, 0.0,1 0.9,1, 0.0,0, 0.0,0