Supervised Single-Label Classification Task
A Supervised Single-Label Classification Task is a supervised classification task that is a single-label classification task.
- AKA: Supervised Unilabel Classification.
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- Counter-Example(s):
- See: Supervised Single-Label Classification System, Supervised Single-Label Classification Algorithm.
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.