In traditional classification problems (single-label), patterns are associated with a single label from the set of disjoint labels. When an example can simultaneously belong to more than one label, we call it a multi-label classification problem. In relation to the learning strategy, the majority of classification methods requires a large number of training instances to be able to generalize the mapping function, making predictions with high accuracy. However, it is usually difficult to find a number of instances labeled which is sufficient to induce an accurate classification model. This problem is enhanced in the multi-label context, since the number of possible combinations in the label attributes increases considerably. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labeled and unlabeled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label classification. This paper proposes three semi-supervised methods for the multi-label classification. In order to validate the feasibility of these methods, an empirical analysis will be conducted, aiming to evaluate the performance of such methods in different tasks and using different evaluation metrics.