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.
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The possibility to ignore parts of the input information does of course also depend on the type of classifier used.
References
Dembczyński, K., Cheng, W., & Hüllermeier, E. (2010). Bayes optimal multilabel classification via probabilistic classifier chains. In International Conference on Machine Learning (pp. 279–286).
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.
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85(3), 333–359.
Tsoumakas, G., & Katakis, I. (2007). Multi label classification: An overview. International Journal of Data Warehouse and Mining, 3(3), 1–13.
Zhu, X., & Wu, X. (2004). Class noise vs. attribute noise: A quantitative study of their impacts. Artificial Intelligence Review, 22(3), 177–210.
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.
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Senge, R., del Coz, J.J., Hüllermeier, E. (2014). On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. In: Spiliopoulou, M., Schmidt-Thieme, L., Janning, R. (eds) Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-01595-8_18
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DOI: https://doi.org/10.1007/978-3-319-01595-8_18
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