کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407542 | 678146 | 2015 | 12 صفحه PDF | دانلود رایگان |
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.
Journal: Neurocomputing - Volume 150, Part B, 20 February 2015, Pages 501–512