I have measurements (survey data) from two different sources. The scaling for one discrete attribute is different.
The values of the attribute in the first survey are: 1, 2, 3
The values of the attribute in the second survey are: 1, 2, 3, 4, 5, 6, 7
Normalized: 1/3, 2/3, 1 and 1/7, 2/7 ... 1
The preprocessed data is then used for machine learning. But the algorithms keep coming up with rules like "If value is 1/7 then ...". I do not want the algorithm to be able to focus on the survey data separately. (Of course it comes up with these reasons because >90% of the data comes from the second survey)
Is there a way to scale attributes so that this is not possible, even if this means a loss of information?
Edit: I guess a shift in feature distributions is inevitable (introducting bias). But I do not know how to keep that to a minimum.