# feature representation for DNA bases classification

I'm currently dealing with large DNA sequences for machine learning purposes, I'm basically improving existing methods.

What I have is several millions of DNA sequences : ACGTAGGCAGGCTTTC ...

In the methods I'm currently reviewing they extracts the features like this : for every nucleobase they put 4 features, the first corresponding to A, the second to C, the third to G and the last to T. If for exemple the current base is G the corresponding 4 features will be 0 0 1 0.

The problem I have with that is that it multiply the number of "effective" feature by four and it will be very sparse.

I was wondering if there would be any disadvantages to put one feature per nucleobase which would be 0, 1, 2, 3 depending on the letter.

It is applied to DNA but my question extend to every kind of discrete features.

A = (1,1,1)