Let us say, I have 4 classification models on a training data set of various examples. Now, I want to choose which 1 out 4 models (or what combination of the 4 models) should I choose to get the final probability for a particular test example. So, basically I want to create a meta model to get ultimate probabilities for all the test examples. I know I can combine all 4 models using regression but I am particularly interested in using a decision tree to combine all 4 models. So, the meta decision tree model will have 4 features/variables (4 model probabilities) as per my current thoughts. Is this a right approach? Should I include more features in it? Please explain how do I combine 4 models using a meta decision tree model to choose the best model out of 4 for a particular test example.  
Pick a model from multiple models using a decision tree
decision trees;ensemble modeling