  Prototype Generation (PG) methods are typically considered for improving the
efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling
high-size corpora. Such approaches aim at generating a reduced version of the
corpus without decreasing the classification performance when compared to the
initial set. Despite their large application in multiclass scenarios, very few
works have addressed the proposal of PG methods for the multilabel space. In
this regard, this work presents the novel adaptation of four multiclass PG
strategies to the multilabel case. These proposals are evaluated with three
multilabel $k$NN-based classifiers, 12 corpora comprising a varied range of
domains and corpus sizes, and different noise scenarios artificially induced in
the data. The results obtained show that the proposed adaptations are capable
of significantly improving -- both in terms of efficiency and classification
performance -- the only reference multilabel PG work in the literature as well
as the case in which no PG method is applied, also presenting a statistically
superior robustness in noisy scenarios. Moreover, these novel PG strategies
allow prioritising either the efficiency or efficacy criteria through its
configuration depending on the target scenario, hence covering a wide area in
the solution space not previously filled by other works.
