I have an extremely unbalanced data set: around 200 positive samples and 70,000 negative samples. To overcome this problem I have tried to over-sample the minority class as suggested in previous questions here.
Since training the classifier (in my case I am using SVM) takes a long time on my computer I first trained a classifier on a small subset of my data picked randomly, the classifier gave solid results when tested on the test set, yet when I trained a classifier using the full training data the classifier produced very poor results. Does anyone have insight into why this is happening and what should i do? I have also tried using weights but it did not produce a different result.