Training and testing AdaBoost for low probability classification I have a dataset that I want to classify as fraud/not fraud and I have many weak learners. My concern is that there is much more fraud than not fraud, so my weak learners perform better than average, but none perform better than 50% accuracy in the complete set.

My question is whether I should set up testing and training sets that are half fraud and half not fraud or if I should just use a representative sample.
Is it possible that adding generated data to your data set will decrease the fraud/non fraud ration and make your dataset more representative / usable ?

At GenieLog, we are producing test data for designing and testing fraud detection tools. We our generator GEDIS Studio we can define regular profiles and fraudster profiles, instantiating each category to a customizable ratio (for ex. 2 % of customer will have fraudulent usage of generated events.)

We did it successfully for telecom CDR (http://www.gedis-studio.com/online-call-detail-records-cdr-generator.html) and Credit Card usages. There's a freely available access to the online generator on http://www.data-generator.com 

I'm pretty sure that even if the tool is not matching your needs at least the approach can be valuable. Otherwise I would be interested to read any objection :)

Regards