On the Stratification of Multi-label Data

  • Konstantinos Sechidis
  • Grigorios Tsoumakas
  • Ioannis Vlahavas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)

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.

Keywords

Average Precision Average Rank Mean Average Precision Binary Relevance Label Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Konstantinos Sechidis
    • 1
  • Grigorios Tsoumakas
    • 1
  • Ioannis Vlahavas
    • 1
  1. 1.Dept. of InformaticsAristotle University of ThessalonikiThessalonikiGreece

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