to fit how classifiers work; giving a geometry problem to a tree, oversized dimension to a kNN and interval data to an SVM are not a good ideas
remove as much nonlinearities as possible; expecting that some classifier will do Fourier analysis inside is rather naive (even if, it will waste a lot of complexity there)
make features generic to all objects so that some sampling in the chain won’t knock them out
check previous works – often transformation used for visualisation or testing similar types of data is already tuned to uncover interesting aspects
avoid unstable, optimizing transformations like PCA which may lead to overfitting