On-line random forests by adding more single Decisions Trees A Random Forest (RF) is created by an ensemble of Decision Trees's (DT). By using bagging, each DT is trained in a different data subset. Hence, is there any way of implementing an on-line random forest by adding more decision tress on new data?

For example, we have 10K samples and train 10 DT's. Then we get 1K samples, and instead of training again the full RF, we add a new DT. The prediction is done now by the Bayesian average of 10+1 DT's.

In addition, if we keep all the previous data, the new DT's can be trained mainly in the new data, where the probability of picking a sample is weighted depending how many times have been already picked.
There's a recent paper on this subject (On-line Random Forests), coming from computer vision. Here's an implementation, and a presentation: Online random forests in 10 minutes