Uni-Target Class Prediction Task

A Uni-Target Class Prediction Task is a class prediction task that is a single-target prediction task (which requires a single value associated to a test case).

• AKA: Single-Label Classification.
• Context:
  • It can range from being a Heuristic Unilabel Classification Task to being a Data-Driven Unilabel Classification Task (such as a supervised unilabel classification task)
• Counter-Example(s):
  • Multi-Label Classification Task.
• See: Single-Target Numeric Value Prediction Task.

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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.

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