The litterature on multi-label learning uses the terms: multi-label learning and multi-label classification. I was wondering what the difference between these terms is, and when to use one over the other??
Both are forms of supervised learning where the classification algorithm should learn from a set of instances/examples.
In multi-class problems each example is restricted to have only one class label.
Multi-label learning is a generalized version in which each instance can belong to multiple classes (e.g. a research paper can belong to both the health and science category).
Binary and multi-class problems can be posed as specific cases of multi-label problem. However, the generality of multi-label problems makes them more difficult than the others.