Let's say I want to detect new species of fish.
I have several models, each trained to recognize a different characteristic, e.g., the speed of known fish, the size of known fish, their known shapes, etc. Naturally, each model will be trained on a distinct set of data: dataset_1 used to train model_speed, dataset_2 used to train model_size, dataset_3 used to train model_shape.
- Is it reasonable to combine the outputs from each of those models and use that combined output as an input to another model, such that together, my 'final' model can better understand how likely it is that it is observing a new type of fish?
- What is the name for such an approach, if it exists?
- How else would I tackle such a problem?