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Title: Dynamic Ensemble Pruning based on Multi-Label Classification
Author(s): F. Markatopoulou, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (12 pages).
Appeared in: Neurocomputing, Elsevier, Volume 150, Part B, pp. 501-512, 2015.
Abstract: Dynamic (also known as instance-based) ensemble pruning selects a (potentially) different subset of models from an ensemble during prediction based on the given unknown instance with the goal of maximizing prediction accuracy. This paper models dynamic ensemble pruning as a multi-label classification task, by considering the members of the ensemble as labels. Multi-label training examples are constructed by evaluating whether ensemble members are accurate or not on the original training set via cross-validation. We show that classification accuracy is maximized when learning algorithms that optimize example-based precision are used in the multi-label classification task. Results comparing the proposed framework against state-of-the-art dynamic ensemble pruning approaches in a variety of datasets using a heterogeneous ensemble of 200 classifiers show that it leads to significantly improved accuracy.
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