# Rationale for Multi-Label vs. Single-Label learning?

I have not seen any research that compares the effects of single-label versus multi-label learning. What I mean by this is not comparing various types of evaluation metrics - such a comparison does not make sense, as single and multi-label learners use different evaluation metrics for test error.

But what I want is an example of how multi-label learning improves the application of classification, in any context. I know the rationale in general is "more, correct information will be better discriminatory information than less information", and I would expect to see better discrimination between classes separated by multi vs. single label learning methods. But where is the empirical evidence?

Can anyone help me out?

Clarification: I am referring to multi-class multi-label learning methods as opposed to normal single-label supervised learners. Examples of each are below:

Multi-class multi-label learners:
multi-label kNN (ML-kNN)
multi-label backpropagation (ML-BP)
rank SVM
binary relevance
classifier chains
random k label sets

Multi-class Single-label learners:
1 vs. 1 SVM, 1 vs. all SVM


Note that there is a subtle but important difference between multilabel problems, in which each instance may belong to several classes, and multiclass problems, in which each instance belongs to one of $\geq 2$ classes. I will discuss both briefly, but based on the question I suspect you are referring to multiclass problems.