Source code - article under review

Abstract: 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.

Keywords: Dynamic ensemble selection, Multi-label learning, Probabilistic Classifier Chains.