Stratied sampling is a sampling method that takes into ...

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

single-label classication tasks, groups are dierentiated based on the

value of the target variable. In multi-label 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 multi-label data context. It considers two stratication methods

for multi-label 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 multi-label datasets.

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