Let's say we have feature A, B, C, D, E to represent one observation in an outlier detection model. We are using scikit-learn outlier detection in our case.
AFAIK, if we normalize all the features, they are as equally important as others.
What if I want to make B very important, e.g. if A, C, D, E increase 20%, the observation might still be considered as normal case while B increases 10%, the observation needs to be marked as an outlier.
Is there any way to increase or decrease features' weight in outlier detection model?