Supervised Multi-Label Classification Task

Context:
- It can be solved by a Supervised Multi-Label Classification System (that implements a Supervised Multi-Label Classification Algorithm).
Example(s):
- a Hierarchical Multilabel Classification Task.
- In Multilabel Text Categorization, each document may belong to several predefined document topics, such as Government and Health.
- In Bioinformatics, each Gene may be associated with more than one Functional Class, such as Metabolism, Transcription and Protein Synthesis.
- In Scene Classification, each Scene Image may belong to more than One Semantic Class, such as Beach and Urban.
- In Text Entity Mention Classification, each Text may contain more than One Entity Mention.
- …
Counter-Example(s):
See: Classification Function, Sequence Classification Task.

References

(Tsoumakas & Katakis, 2007) ⇒ Grigorios Tsoumakas, and Ioannis Katakis. (2007). “Multi-Label Classification: An Overview.” In: International Journal of Data Warehousing and Mining, 3(3). doi:10.4018/jdwm.2007070101
- QUOTE: 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 [math]\displaystyle{ L }[/math], [math]\displaystyle{ \mid L \mid \gt 1 }[/math]. If [math]\displaystyle{ \mid L \mid = 2 }[/math], then the learning problem is called a binary classification problem (or filtering in the case of textual and web data), while if [math]\displaystyle{ \mid L \mid \gt 2 }[/math], then it is called a multi-class classification problem.
  In multi-label classification, the examples are associated with a set of labels [math]\displaystyle{ Y ⊆ L }[/math]. In the past, multi-label classification was mainly motivated by the tasks of text categorization and medical diagnosis.