Advertisement

On the Problem of Error Propagation in Classifier Chains for Multi-label Classification

  • Robin Senge
  • Juan José del Coz
  • Eyke Hüllermeier
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

So-called classifier chains have recently been proposed as an appealing method for tackling the multi-label classification task. In this paper, we analyze the influence of a potential pitfall of the learning process, namely the discrepancy between the feature spaces used in training and testing: while true class labels are used as supplementary attributes for training the binary models along the chain, the same models need to rely on estimations of these labels when making a prediction. We provide first experimental results suggesting that the attribute noise thus created can affect the overall prediction performance of a classifier chain.

Notes

Acknowledgements

This research has been supported by the Germany Research Foundation (DFG) and the Spanish Ministerio de Ciencia e Innovación (MICINN) under grant TIN2011-23558.

References

  1. Dembczyński, K., Cheng, W., & Hüllermeier, E. (2010). Bayes optimal multilabel classification via probabilistic classifier chains. In International Conference on Machine Learning (pp. 279–286).Google Scholar
  2. Dembczynski, K., Waegeman, W., Cheng, W., & Hüllermeier, E. (2012). On label dependence and loss minimization in multi-label classification. Machine Learning, 88(1–2), 5–45.MathSciNetCrossRefMATHGoogle Scholar
  3. Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333–359.MathSciNetCrossRefGoogle Scholar
  4. Tsoumakas, G., & Katakis, I. (2007). Multi label classification: An overview. International Journal of Data Warehouse and Mining, 3(3), 1–13.CrossRefGoogle Scholar
  5. Zhu, X., & Wu, X. (2004). Class noise vs. attribute noise: A quantitative study of their impacts. Artificial Intelligence Review, 22(3), 177–210.MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Robin Senge
    • 1
  • Juan José del Coz
    • 2
  • Eyke Hüllermeier
    • 1
  1. 1.Philipps-Universität MarburgMarburgGermany
  2. 2.University of OviedoGijónSpain

Personalised recommendations