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