At this moment I have a dataset with 4000 samples (50% positive and 50% negative). Normally I would do cross validation for this approach, however besides normal data mining techniques I am also trying an alternate ILP approach.
Since I can't implement cross validation using the ILP system I am using, to maintain dataset coherence between the two types of techniques I decided to instead do a split validation. I keep hearing about split validation should only be used in large datasets, but how large should they be? Would implement it on this one produce unacceptable "bad" results? Should I really avoid this approach?