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Stratied sampling is a sampling method that takes into
account the existence of disjoint groups within a population and produces
samples where the proportion of these groups is maintained. In
singlelabel classication tasks, groups are dierentiated based on the
value of the target variable. In multilabel learning tasks, however, where
there are multiple target variables, it is not clear how stratied sampling
could/should be performed. This paper investigates stratication
in the multilabel data context. It considers two stratication methods
for multilabel data and empirically compares them along with random
sampling on a number of datasets and based on a number of evaluation
criteria. The results reveal some interesting conclusions with respect to
the utility of each method for particular types of multilabel datasets.
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