*2.1.1 Problem transformation*

Binary relevance (BR) is used to address a multilabel problem with a binary classifier, and its advantages are simplicity and efficiency, but correlation between labels is not considered. Classifier chains (CCs) are configured in a chain of binary classifiers where a classifier in the chain is based on the prediction of the previous classifier; their advantage is that they consider the relationship between labels but

**Figure 1.** *Taxonomy of multilabel learning algorithms [15].*

#### *Multilabel Classification Based on Graph Neural Networks DOI: http://dx.doi.org/10.5772/intechopen.99681*

hence cannot be parallelized. Calibrated label ranking (CAL) performs ranking via the pairwise comparison of labels and has the advantage of considering the relationship (but only the pairwise relationship) between labels. Label powersets (LP) treat the situation when multiple labels belong to the same sample as a new label and have the advantage of considering the relationship between labels, but the time complexity grows exponentially with label sets. Random k-labelsets (RKL) are variants of LP models where each classifier is trained with a small random set of labels; their advantage is that they consider the relationship between labels, but they have a low accuracy rate if a worse label set combination is randomly selected.

#### *2.1.2 Adapted algorithm*

The multilabel *k*-nearest neighbor (MLkNN) method is derived from the traditional *k*-nearest neighbor algorithm. Each sample is identified with *k* nearest neighbors in the training set, and information is obtained from these identified neighbors. Multilabel support vector machine (ML-SVM) classification determines an optimal hyperplane that separates observations according to their labels. A multilabel decision tree (ML-DT) is constructed by building a decision tree, where each node corresponds to a set of samples in the data set.
