Using confidence values in multi-label classification problems with semi-supervised learning

In most traditional classification methods, each instance is associated with one single nominal target variable (single-label problems). However, there are also cases where an instance can be associated with more than one label simultaneously, referring to as multi-label classification problems. One of the main problems with classification methods is that many of these require a high number of instances to be able to generalize the mapping function, making predictions with high accuracy. In order to smooth out this problem, the idea of semi-supervised learning has emerged. It combines labeled and unlabelled data during the training phase. However, in semi-supervised learning, it is important to define an efficient process of assignments of instances. This paper proposes three semi-supervised methods for the multilabel classification, focusing on the use of a confidence parameter in the process of automatic assignment of labels. 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 situations, besides the use of different evaluation metrics on this performance.