I have a list of some 100 millions of strings, each of different length. Examples:
As you can see the strings vary in length and some can include english words, some not, some both words and random characters. All use the characters [a-z]. It is possible to characterise the strings are being humanly created or by a algorithm with a reasonable certainty.
Later I aim to also classify between different sub-types of algorithms generating these strings (multiclass classification).
The 100 millions strings I have are already labeled, based on other analysis. But because this is a very slow task, I will use Machine Learning to try and predict the origin of new strings as we get new data.
The methods I have started with are
Long Short Term Memory (LSTM) and
Gated Recurrent Unit (GRU). I will eventually also run trigrams.
As both time and accuracy are very important factors of the method I eventually choose, I want to make sure I test as many as possible. But since there is a forest of methods, I hope to get some helpful advice here.
I imagine methods based on features, such as
Random Forest and
Support Vector Machine could be useful, but I haven't them used before, and I am not certain if they or other methods could handle 100 millions of strings in an efficient way.
Any suggestions for methods to look at are very welcomed.