Dealing with unbalanced error rate in confusion matrix
Welcome to the site!

I think Ensemble Method is very tricky. when one of the model doesn't work well then the accuracy of the Ensemble also goes down.

For instance let us consider that you are using RandomForest(RF) and Rpart for classification and RF accuracy is 90% and Rpart accuracy is 60%. If you ensemble these 2 models then the Ensemble accuracy goes down.

Coming to your scenario, you need to be very careful at the time of stacking, you need to select the models that are performing moderately and then stack them to improve the accuracy.

How is the distribution of 0/1's, if they are imbalanced then you need to balance to improve the accuracy of the model. To handle imbalance data situation we use packages like SMOTE,ROSE etc.

Feature Engineering like adding external factors or adding new features, might help you to improve your models accuracy.

Do let me know if you have any additional questions.