Improving the robustness of bagging with reduced sampling size
Entity
UAM. Departamento de Ingeniería InformáticaPublisher
Université catholique de LouvainDate
2014Citation
ESANN 2014: proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges: Université catholique de Louvain, 2014. 677-682ISBN
978-287419095-7Funded by
The authors acknowledge financial support from the Spanish Dirección General de Investigación, project TIN2010-21575-C02-02Editor's Version
https://www.elen.ucl.ac.be/esann/proceedings/papers.php?ann=2014Subjects
InformáticaNote
This is an electronic version of the paper presented at the 22th European Symposium on Artificial Neural Networks, held in Bruges on 2014Abstract
Bagging is a simple and robust classification algorithm in
the presence of class label noise. This algorithm builds an ensemble of
classifiers by bootstrapping samples with replacement of size equal to the
original training set. However, several studies have shown that this choice
of sampling size is arbitrary in terms of generalization performance of
the ensemble. In this study we discuss how small sampling ratios can
contribute to the robustness of bagging in the presence of class label noise.
An empirical analysis on two datasets is carried out using different noise
rates and bootstrap sampling sizes. The results show that, for the studied
datasets, sampling rates of 20% clearly improve the performance of the
bagging ensembles in the presence of class label noise.
Files in this item
Google Scholar:Sabzevari, Maryam
-
Martínez Muñoz, Gonzalo
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Suárez González, Alberto
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