Abstract
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative experimental results of certain multilabel classification methods. It also contributes the definition of concepts for the quantification of the multi-label nature of a data set.
Citation impact
2,476
total citations
- FWCI
- 68.75
- Percentile
- 100%
- References
- 22
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Computer science
- Multi-label classification
- Categorization
- Task (project management)
- Artificial intelligence
- Set (abstract data type)
- Function (biology)
- Text categorization
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