Decision Tree Ensembling
Popular ways to combine trained models are schema's based on voting: predictions are treated as weighted votes, the class with the majority of votes is selected as ultimate prediction, and/or stacking: predictions are treated as features in a newly trained model. 

Depending on the domain at hand one of this approaches might help you. Stacking (depending on the metalearner of choice) introduces more freedom and might introduce a complexity that is not warranted by the concept you are searching for, while voting won't help much when identifying subdomain fitted for perticular problems in your search space. 

Also when combining multiple classifiers it might be worth your while to try out some fundamentally different approaches.