The core idea of Classifier Chains is to decompose the multi-label classification problem and convert it into the form of a binary classifier chain. The construction of the binary classifier after the chain is based on the prediction results of the previous classifier; When constructing the model, the label order is first shuffled and sorted, and then the model corresponding to each label is constructed from beginning to end.

Advantage:
Considering the dependency between tags, the pan-China capability of the final model is better than the model built by Binary Relevance.

Shortcoming:
It is difficult to find a more appropriate dependency between tags.

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•	Binary Relevance (first-order)
•	Classifier Chains (high-order)
•	Calibrated Label Ranking (second-order)

Problem Transformation Methods of Multi-Label Classification

The core idea of Binary Relevance is to decompose the multi-label classification problem and transform it into q binary classification problems, where each binary classifier corresponds to a label to be predicted.

Advantage:
The realization method is simple and easy to understand;
When there is no mutual dependence between the y values, the model works well

Shortcoming:
If there is a mutual dependence between y, then the generalization ability of the final model is relatively weak;
It is necessary to construct q two classifiers, where q is the number of y values to be predicted. When q is large, there are correspondingly more models to be constructed.

Binary Relevance

Classifier Chains

The core idea of Calibrated Label Ranking is to decompose the multi-label classification problem and convert it into a label sorting problem. The final label is the largest label value after sorting.

Advantage:
Considering the combination of tags, the final model has a relatively good generalization ability.

Shortcoming:
Only the combination of two tags is considered, and all the dependencies between tags are not considered.

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