Paper Details

  • Title:

    On the Stratification of Multi-Label Data

  • Author(s):

    K. Sechidis, Grigorios Tsoumakas, I. Vlahavas

  • Keywords: -
  • Abstract:

    Stratified 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 classification tasks, groups are differentiated 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 stratified sampling could/should be performed. This paper investigates stratification in the multi-label data context. It considers two stratification 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.

  • Category: Conference Papers
  • Tags: 2011 Sechidis Tsoumakas Vlahavas