Multi-label classification
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Multi-label classification is a concept in mathematics and machine learning. Traditional single-label classification is concerned with learning from a set of examples that are associated with a single label l from a set of disjoint labels L, | L | > 1. In multi-label classification, the examples are associated with a set of labels 
. In the past, multi-label classification was mainly motivated by the tasks of text categorization and medical diagnosis. Nowadays, we notice that multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization and semantic scene classification.
[edit] Further reading
- Collective Multi-Label Text Classification, at University of Massachusetts, Amherst
- A preliminary approach to the multilabel classification problem of Portuguese juridical documents, at Universidade de Evora, Portugal
- Multi-label machine learning and its application to semantic scene classifcation, at College of William and Mary, US
