Multi-label classification
From Wikipedia, the free encyclopedia
 |
This article or section is in need of attention from an expert on the subject. Please help recruit one or improve this article yourself. See the talk page for details. Please consider using {{Expert-subject}} to associate this request with a WikiProject |
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
