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, number of organizations, number of followers). Data is incomplete, some fields can be missing in some profiles. Training set is limited (say, 300 examples).

I have two strategies of feature engineering, and I don't know which one to use.

1. Expand features: take 40 features (Profile 1 age, Profile 1 job title, ..., Profile 10 age, Profile 10 job title).

2. 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 maximum number of organizations, take sum of numbers of followers.

What strategy is generally should give best results and why?

P.S. Should questions like this go to this site or CrossValidated.SE is a better fit?

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.

1. Expand features: take 40 features (Profile 1 age, Profile 1 job title, ..., Profile 10 age, Profile 10 job title).

2. 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?