As an example. If you are tying to classify humans from dogs. Is it possible to approach this problem by classifying different kinds of animals (birds, fish, reptiles, mammals, ...) or even smaller subsets (dogs, cats, whales, lions, ...)
Then when you try to classify a new data set, anything that did not fall into one of those classes can be considered a human.
If this is possible, are there any benefits into breaking a binary class problem into several classes (or perhaps labels)?
Benefits I am looking into are: accuracy/precision of the classifier, parallel learning.