MLKD logo   Machine Learning &
Knowledge Discovery Group

Publication Details

  Author(s): F. Markatopoulou, G. Tsoumakas, I. Vlahavas.

Title: “Dynamic Ensemble Pruning based on Multi-Label Classification”.

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.

Relevant Links: