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 <math>l</math> from a set of disjoint labels <math>L, |L| > 1 </math>. In multi-label classification, the examples are associated with a set of labels <math>Y \subseteq L</math>.

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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 <math>l</math> from a set of disjoint labels <math>L, |L| > 1 </math>. In multi-label classification, the examples are associated with a set of labels <math>Y \subseteq L</math>. 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.
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  • November 2008
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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 <math>l</math> from a set of disjoint labels <math>L, |L| > 1 </math>. In multi-label classification, the examples are associated with a set of labels <math>Y \subseteq L</math>.
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  • Multi-label classification
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