# Random Forest closed loop

I'm using the Random Forest algorithm for classification. I have some variable to use as features in input, but I was wondering if I can use the output of the classification itself as input.

Suppose, for example, that I want to classify people in two categories : "criminal" or "honest". In order to do that, I have a vector of three features: the name of the city where the person is born, the number of people that committed crimes in the city where the person is born, and the number of people that live in that city. When I run the algorithm, I will get the classification results $y_1,y_2,...,y_n$ for each input vector $x_1,x_2,...,x_n$.

Now, it would be wrong to add to the input vector a new feature "number of people from the same city who have been classified by the algorithm as criminal"?

My purpose is to run several instances of the algorithm on different datasets and then share the informations gathered in each subset between the different instances. Does it can make sense?