Mining Multi-label Data

Abstract

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.