# Classifier Chains for Multilabel Classification

## Multi-label problem

In some classification problems, we have multilabel labels to be predicted. Many different approaches are proposed to solve such problems.

### Algorithm Level

Develop algorithms for multilabel problems, such as

- Decision trees,
- AdaBoost.

### Problem Transformation

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.

#### Classifier Chain

Classifer chains, aka CC, trains $l$ classifiers where $l$ is the number of labels for each record.

`wiki/machine-learning/classification/classifier-chains`

:`wiki/machine-learning/classification/classifier-chains`

Links to:LM (2021). 'Classifier Chains for Multilabel Classification', Datumorphism, 03 April. Available at: https://datumorphism.leima.is/wiki/machine-learning/classification/classifier-chains/.