Mining Multi-label Data
- Grigorios TsoumakasAffiliated withDept. of Informatics, Aristotle University of Thessaloniki Email author
- , Ioannis KatakisAffiliated withDept. of Informatics, Aristotle University of Thessaloniki
- , Ioannis VlahavasAffiliated withDept. of Informatics, Aristotle University of Thessaloniki
A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label λ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.
Textual data, such as documents and web pages, are frequently annotated with more than a single label. For example, a news article concerning the reactions of the Christian church to the release of the “Da Vinci Code” film can be labeled as both religion and movies. The categorization of textual data is perhaps the dominant multi-label application.
- Mining Multi-label Data
- Book Title
- Data Mining and Knowledge Discovery Handbook
- pp 667-685
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Springer Science+Business Media, LLC
- Additional Links
- Industry Sectors
- eBook Packages
- Editor Affiliations
- ID1. , Dept. Industrial Engineering, Tel Aviv University
- ID2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev
- Author Affiliations
- 1. Dept. of Informatics, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
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