Dynamic ensemble selection (DES) is the problem of finding, given an input x, a subset of models among the ensemble that achieves the best possible prediction accuracy. Recent studies have reformulated the DES problem as a multi-label classification problem and promising performance gains have been reported. However, their approaches may converge to an incorrect, and hence suboptimal, solution as they don’t optimize the true - but non standard - loss function directly. In this paper, we show that the label dependencies have to be captured explicitly and propose a DES method based on Probabilistic Classifier Chains. Experimental results on 20 benchmark data sets show the effectiveness of the proposed method against competitive alternatives, including the aforementioned multi-label approaches. This study is reproducible and the source code has been made available online (https://github.com/naranil/pcc_des).
CITATION STYLE
Narassiguin, A., Elghazel, H., & Aussem, A. (2017). Dynamic Ensemble Selection with Probabilistic Classifier Chains. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10534 LNAI, pp. 169–186). Springer Verlag. https://doi.org/10.1007/978-3-319-71249-9_11
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