# Supervised Multi-Label Classification Task

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A Supervised Multi-Label Classification Task is supervised classification task that is a multilabel 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

### 2007

- (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.

- 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.

### 2006

- (Zhang & Zhou, 2006) ⇒ Min-Ling Zhang, and Zhi-Hua Zhou. (2006). “Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization.” In: IEEE Transactions on Knowledge and Data Engineering, 18(10). [doi:10.1109/TKDE.2006.162].

### 2005

- (McDonald et al., 2005) ⇒ Ryan McDonald, Koby Crammer, and Fernando Pereira. (2005). “Flexible Text Segmentation with Structured Multilabel Classification.” In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP, 2005).

### 2004

- (Boutell et al., 2004) ⇒ Matthew R. Boutell, Jiebo Luo, Xipeng Shen, and Christopher M. Brown. (2004). “Learning Multi-label Scene Classification.” In: Pattern recognition Journal, 37(9). doi:10.1016/j.patcog.2004.03.009