You haven't stated your reason for wanting to do this, nor what algorithm you are using, both of which will affect whether your proposed tactic "makes sense". I'll give you a couple reasons why the tactic may be a Bad Idea.
First, data are divided into training and testing data for a reason: so you have a sense for how the classifier will generalize (i.e., to estimate it's true classification accuracy). Once you start polluting your training data with the testing data, you should have less confidence in the classifier's generalized performance. On one hand, I would expect that adding high confidence testing observations to the training data would increase training accuracy. On the other hand, your testing data will now have a larger proportion of low confidence observations, which will reduce your testing accuracy. So how would you judge the ability of the classifier to generalize?
When you add the high confidence test observations to your training set, you are teaching the classifier something it already knows. Depending on your classifier, there is a good chance that by doing this your are biasing the classifier farther away from the outliers (the 5% initially misclassified) and by doing so, reducing the true (general) classification accuracy. In fact, there are algorithms designed to do just the opposite of what you are proposing: they instead place more significance on the misclassified (low probability) observations to improve the accuracy of the classifier. For an example of this, take a look at boosting.