Abstract
The defining characteristic of multi-label as opposed to single-label data is that each instance can belong to several classes at once. The multi-label classification task is to predict all relevant labels of a target instance. This chapter presents and experimentally evaluates our FRONEC method, the Fuzzy Rough NEighbourhood Consensus.
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CITATION STYLE
APA
Vluymans, S. (2019). Multi-label Learning. In Studies in Computational Intelligence (Vol. 807, pp. 189–218). Springer Verlag. https://doi.org/10.1007/978-3-030-04663-7_7
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