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I want to build feature vectors from data of my test set, which contains profiles of people. I always want to compare two profiles to each other.

Thus my features are:
- Same surname ∈ {undefined, yes, no}
- age delta ∈ {undefined, x | x ∈ Z}
- number of same interests ∈ N
- genders ∈ {(male, female), (male, male), (undefined, male), ...}
- number of common friends (i think this should be normalized by the total number of friends both profiles have) ∈ N

I want to use this feature vectors labeled with 1, -1 to learn classifying a relation between two profiles with a SVM or k-nearest neighbours. I think I should binarize the feature vectors somehow, but I am not shure what is the best way.

My ideas are:
- Just transform the values into binary representation
- Use One-hot encoding
- Split the gender feature into two features: gender_A, gender_B
- Normalize the common friends value by dividing through the absolute of the difference of number of friends for each profile plus one
- Don't normalize the common friends value, just add more features for #friends_A, #friends_B

What do you think would be the best solution or what could I do instead?
Can anyone help me?

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  • $\begingroup$ It seems that you have graph values, probably from a social network. I would add some edges or vertex feature like centrality, betweeness, etc... $\endgroup$
    – Scratch
    Commented Mar 26, 2014 at 14:51
  • $\begingroup$ That's correct. Centrality and betweeness already are another project. Actually I'm only not sure about the representation of the feature vector. $\endgroup$
    – Thorben
    Commented Mar 26, 2014 at 22:08

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As for the number of common friend i suggest using the Jacard index. Its basically the ratio between shared friends both friends.

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