I have a problem with a number of inputs and one binary output, which I have tried to train several classifiers to solve. Unfortunately, none of the classifiers (MLP, SVM, bagging) have achieved the required level of accuracy.
I am thinking of subdividing the problem by splitting the dataset into two, and using a different classifier on each half, or the same classifier but with different parameters (or the same parameters, but it will be learning a different problem).
One approach is to choose the attribute which has the highest correlation with the output, and split the data so that the lowest values are in one set, and the highest in another. I fond that one set gave very good accuracy, whereas the other had a lower accuracy than the combined data set.
Is there an alternative approach to subdividing the problem that anyone can suggest?