All the techniques discussed in the previous chapters assumed that each example is labeled with one and only one class. In realistic applications, however, this is not always the case. Quite often, an example is known to belong to two or more classes at the same time, sometimes to many classes. For machine learning, this poses certain new problems. After a brief discussion of how to deal with this issue within the framework of classical paradigms, this chapter describes the currently most popular approach: binary relevance.
CITATION STYLE
Kubat, M. (2017). Induction in Multi-Label Domains. In An Introduction to Machine Learning (pp. 251–271). Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0_13
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