I've had an idea of a training scheme for multiple machine learning models, and want to know if it makes sense or it already has a name. The idea is to train models kinda like a swarm mind (I was watching this video when thought of that).
The outline of an algorithm is as follows and seems pretty simple: for every batch of data, pick a sample of models and train each model in order with additional features that contain predictions of every previous model on that batch, take another batch and repeat.
Of course there are a lot of variations of how to train each individual model, how to use predictions as features, when to stop and so on. I just wanna know a bit more before starting experimenting with that.