# adding logic combinations of boolean features in classification

I want to build a classifier from a dataset of vectors that include exclusively boolean values. Is there any chances that my classifier might perform better if, previously to the learning, I add features that consist in combinations (with logic operators « OR » and « AND ») of the original ones or would that be pointless ?

For example I have the following dataset :

            feature A   feature B   feature C
vector 1    True        False       True
vector 2    True        True        False
vector 3    False       True        True


Imagine I have the feeling that the fact that a data has True for both feature C and feature D would typically make it go in one particular class. Before learning the datas to build the classifier, should I add to each vector en extra feature computed by the logical operation « feature B AND feature C » :

            feature A   feature B   feature C   feature D
vector 1    True        False       True        False
vector 2    True        True        False       False
vector 3    False       True        True        True


Or would the relevance of « feature B AND feature C » be taken into account by the classifier anyway ? Does it depend on the algorithm (svm, Knn, etc. ) ?