The Binary Relevance (BR) approach breaks down a multi-label classification problem into a sequence of binary classification problems, one for each label y j in the dataset. In particular, a distinct binary classification issue is generated for every label y j . If y j appears in the matching multi-label example, the label for each example in the training set is regarded as positive; if not, it is seen as negative. By combining the classification results from all the binary classifiers, we can determine the final labels for each multi-label example.