I have 20k samples in two classes namely
negative. Samples are formed of 10 digits and within each sample a digit is used exactly once. For instance;
9876543102 are proper samples as each digit is used once. Order of the digits are what makes samples
negative. A classification statement for instance,
positive samples can have
negatives can't. Or a more intricate one like, 90% percent of positive samples have leading 0's and none of
negatives have this. For the remanining 10% percent,
315 are patterns that makes samples positive.
There is a total of
10!=3.6m possible samples. So, in order to carry out a binary classification, what should be the proper ML approach? Can LSTM's handle this? Maybe a primitive Neural Network with 10 inputs and a binary classification? Or I should look for a different ML approach? Thanks.