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. ) ?
Try it and see what happens. Neural networks don't have enough representational power to learn an XOR operation without at least one hidden layer, so there are definitely some interesting features you can construct with logical operations. The AND operation is equivalent to multiplication, which corresponds to interaction terms linear models.

But yeah, it does depend on the model. For example, a decision tree can learn these kinds of features on its own (although it won't necessarily). For exame, an AND operation would correspond to two tests on the same branch.