dc.description.resumo | In this paper, we investigate two important
problems in multi-label classification algorithms, which are:
the number of labeled instances and the high dimensionality of
the labeled instances. In the literature, we can find several
papers about multi-label classification problems, where an
instance can be associated with more than one label
simultaneously. One of the main issues with multi-label
classification methods is that many of these require a high
number of instances to be able to generalize in an efficient way.
In order to solve this problem, we used semi-supervised
learning, which combines labeled and unlabeled instances
during the training process. In this sense, the semi-supervised
learning may become an essential tool to define, efficiently, the
process of automatic assignment of labels. Therefore, this
paper presents four 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 using high-dimensional datasets,
aiming to evaluate the performance of such methods in
different situations. In this case, we will apply a feature
selection algorithm in order to reduce, in an efficient way, the
number of features to be used by the classification methods. | pt_BR |