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EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data

Vluymans, Sarah; Triguero, Isaac; Cornelis, Chris; Saeys, Yvan

Authors

Sarah Vluymans

Chris Cornelis

Yvan Saeys



Abstract

Classification problems with an imbalanced class distribution have received an increased amount of attention within the machine learning community over the last decade. They are encountered in a growing number of real-world situations and pose a challenge to standard machine learning techniques. We propose a new hybrid method specifically tailored to handle class imbalance, called EPRENNID. It performs an evolutionary prototype reduction focused on providing diverse solutions to prevent the method from overfitting the training set. It also allows us to explicitly reduce the underrepresented class, which the most common preprocessing solutions handling class imbalance usually protect. As part of the experimental study, we show that the proposed prototype reduction method outperforms state-of-the-art preprocessing techniques. The preprocessing step yields multiple prototype sets that are later used in an ensemble, performing a weighted voting scheme with the nearest neighbor classifier. EPRENNID is experimentally shown to significantly outperform previous proposals.

Citation

Vluymans, S., Triguero, I., Cornelis, C., & Saeys, Y. (2016). EPRENNID: An evolutionary prototype reduction based ensemble for nearest neighbor classification of imbalanced data. Neurocomputing, 216, https://doi.org/10.1016/j.neucom.2016.08.026

Journal Article Type Article
Acceptance Date Aug 4, 2016
Online Publication Date Aug 11, 2016
Publication Date Dec 5, 2016
Deposit Date Aug 26, 2016
Publicly Available Date Aug 26, 2016
Journal Neurocomputing
Print ISSN 0925-2312
Electronic ISSN 0925-2312
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 216
DOI https://doi.org/10.1016/j.neucom.2016.08.026
Keywords Imbalanced data; Prototype selection; Prototype generation;
Differential evolution; Nearest neighbor
Public URL http://eprints.nottingham.ac.uk/id/eprint/36055
Publisher URL http://www.sciencedirect.com/science/article/pii/S0925231216308669
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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