Is it common to have a different feature set for samples in the train set and in the test set?
My use case is text categorization: when training, I use the words as the features. But when testing, I want to add hypernyms.
For example, if the training set contained the sentence "I eat a fruit", and there is a test sentence "I eat an apple", then I want to have the word "fruit" as a feature of the test sentence, in addition to its words, so that it will be classified positive. However, I don't want to add those hypernyms in the training set - if the training set contained only "I eat an apple", I don't want the sentence "I eat a fruit" to be classified positive.
So, I thought of having a small feature set for training, and a larger feature set for testing.
Is this common? If so, I would be happy to have some references.