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. ) ?