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Improving Bagging Ensembles for Class Imbalanced Data by Active Learning

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Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

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Abstract

Extensions of under-sampling bagging ensemble classifiers for class imbalanced data are considered. We propose a two phase approach, called Actively Balanced Bagging, which aims to improve recognition of minority and majority classes with respect to so far proposed extensions of bagging. Its key idea consists in additional improving of an under-sampling bagging classifier (learned in the first phase) by updating in the second phase the bootstrap samples with a limited number of examples selected according to an active learning strategy. The results of an experimental evaluation of Actively Balanced Bagging show that this approach improves predictions of the two different baseline variants of under-sampling bagging. The other experiments demonstrate the differentiated influence of four active selection strategies on the final results and the role of tuning main parameters of the ensemble.

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Notes

  1. 1.

    For simplicity margin will be denoted as m - in particular in experiments see Tables 3.3,  3.4,  3.5 and 3.6; further introduced weight update methods will be denoted analogously.

  2. 2.

    http://www.ics.uci.edu/ mlearn/MLRepository.html.

  3. 3.

    We are grateful to prof. W. Michalowski and the MET Research Group from the University of Ottawa for providing us an access to scrotal-pain data set.

  4. 4.

    Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition, 2016.

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Acknowledgements

The research was supported from Poznań University of Technology Statutory Funds.

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  1. Institute of Computing Science, Poznań University of Technology, Piotrowo 2, 60-965, Poznań, Poland

    Jerzy Błaszczyński & Jerzy Stefanowski

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  1. Jerzy Błaszczyński
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  2. Jerzy Stefanowski
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Correspondence to Jerzy Błaszczyński .

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  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Błaszczyński, J., Stefanowski, J. (2018). Improving Bagging Ensembles for Class Imbalanced Data by Active Learning. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_3

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