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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??

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    – gnat
    Jul 23, 2015 at 17:15
  • @gnat The question is specific to multi-label learning, a a field within machine-learning, which is a field within artificial intelligence. The question is simple for those who know something about this field. Do you tell someone who asks a question about C#, or some other programming language - which you may not know anything about, to share what they know about that particular field?
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    Jul 23, 2015 at 17:52
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    Jul 23, 2015 at 18:12

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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.

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  • Thanks for your answer. Is the term "multi-label classification" used for binary problems (within multi-label problems), and "multi-label learning" for a multi-class problems (within multi-label problems)? For instance, Mulan (mulan.sourceforge.net) is "A Java Library for Multi-Label Learning", but you can use it for multi-label classification.
    – Kenci
    Jul 24, 2015 at 11:35
  • @Kenci As far as I know binary classification problems are a special case of multi-class classification problems. Multi-class classification problems are a special case of multi-labelled classification. You can take a look at A Literature Survey on Algorithms for Multi-label Learning for more details.
    – manlio
    Jul 24, 2015 at 16:42

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