  Multilabel classification is a relatively recent subfield of machine
learning. Unlike to the classical approach, where instances are labeled with
only one category, in multilabel classification, an arbitrary number of
categories is chosen to label an instance. Due to the problem complexity (the
solution is one among an exponential number of alternatives), a very common
solution (the binary method) is frequently used, learning a binary classifier
for every category, and combining them all afterwards. The assumption taken in
this solution is not realistic, and in this work we give examples where the
decisions for all the labels are not taken independently, and thus, a
supervised approach should learn those existing relationships among categories
to make a better classification. Therefore, we show here a generic methodology
that can improve the results obtained by a set of independent probabilistic
binary classifiers, by using a combination procedure with a classifier trained
on the co-occurrences of the labels. We show an exhaustive experimentation in
three different standard corpora of labeled documents (Reuters-21578,
Ohsumed-23 and RCV1), which present noticeable improvements in all of them,
when using our methodology, in three probabilistic base classifiers.
