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

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


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.



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


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

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

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