I have a classification task for people with 3 categories. I want to apply machine learning for that. I have 10 sources of data, which have the same fields (say 4: age, job title, a number of organizations, a number of followers). Data is incomplete, some fields can be missing in some profiles. The training set is limited (say, 300 examples).
I have two strategies for feature engineering, and I don't know which one to use.
Expand features: take 40 features (Profile 1 age, Profile 1 job title, ..., Profile 10 age, Profile 10 job title).
Compact features: take 4 features, and apply some heuristics to merge the values from different profiles. Say, take age and job title which occur most frequently, take a maximum number of organizations, take a sum of numbers of followers.
What strategy is generally used to give best results and why?
