Algorithm adaptation is a way to directly apply the existing single label algorithm to multiple labels, mainly including:

Multi-Label KNN：

For each instance, firstly, obtain the K instances closest to it, and then use the label set of these instances to judge the value of the predicted label set of this instance through the maximum a posteriori probability (map).

Multi-Label Decision Tree：

The core of using the decision tree to deal with multi-label content is to give more fine-grained information entropy gain criteria to build this decision tree model.