Random Forest closed loop
This is a cascade classifier. Of course you can do that, but the main disadvantage (that you have to keep in mind) is that your error propagates along your cascade classifiers. So even if you have a very good classifier somewhere towards the end of your cascade, if you feed it with weak features (i.e., the output of the previous layer which was not so good) you should not expect to get a reasonable answer. There is generally another common problem with this method and that's the well-known overfitting effect. However, since you are using different subsets of data to train each classifier it shouldn't be a problem in your case I guess.