In some classification problems, we have multilabel labels to be predicted. Many different approaches are proposed to solve such problems.
Develop algorithms for multilabel problems, such as
- Decision trees,
On problem or data level, we can transform the multi-label problem to one or more single label problems.
Binary Relevance Method
Binary relevance method, aka BM, transforms the problem into a single label problem by training a binary classifier for each label.
By doing so, the correlations between the target labels are lost.
Label Combination Method
Label combination method (label power-set method), aka CM, combines the labels into single labels.
Classifer chains, aka CC, trains $l$ classifiers where $l$ is the number of labels for each record.
LM (2021). 'Classifier Chains for Multilabel Classification', Datumorphism, 03 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/classification/classifier-chains/.
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