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New Ordering-Based Pruning Metrics for Ensembles of Classifiers in Imbalanced Datasets Read chapter Read first chapter

2016 | OriginalPaper | Chapter

On Properties of Undersampling Bagging and Its Extensions for Imbalanced Data

Author : Jerzy Stefanowski

Published in: Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Publisher: Springer International Publishing

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Abstract

Undersampling bagging ensembles specialized for class imbalanced data are considered. Particular attention is paid to Roughly Balanced Bagging, as it leads to better classification performance than other extensions of bagging. We experimentally analyze its properties with respect to bootstrap construction, deciding on the number of component classifiers, their diversity, and ability to deal with the most difficult types of the minority examples. We also discuss further extensions of undersampling bagging, where the data difficulty factors influence sampling examples into bootstraps.

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Footnotes
1
We are grateful to our Master students Lukasz Idkowiak and Mateusz Lango for their help in implementing and testing these algorithms.
 
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Metadata
Title
On Properties of Undersampling Bagging and Its Extensions for Imbalanced Data
Author
Jerzy Stefanowski
Copyright Year
2016
Book
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015

Print ISBN: 978-3-319-26225-3

Electronic ISBN: 978-3-319-26227-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-26227-7_38

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