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.
CITATION STYLE
Senge, R., Delcoz, J. J., & Hüllermeier, E. (2014). On the problem of error propagation in classifier chains for multi-label classification. In Studies in Classification, Data Analysis, and Knowledge Organization (Vol. 47, pp. 163–170). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-01595-8_18
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