Classifier Chains for Multilabel Classification
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/.