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RHSBoost: Improving classification performance in imbalance data

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  • Gong, Joonho
  • Kim, Hyunjoong

Abstract

Imbalance data are defined as a dataset whose proportion of classes is severely skewed. Classification performance of existing models tends to deteriorate due to class distribution imbalance. In addition, over-representation by majority classes prevents a classifier from paying attention to minority classes, which are generally more interesting. An effective ensemble classification method called RHSBoost has been proposed to address the imbalance classification problem. This classification rule uses random undersampling and ROSE sampling under a boosting scheme. According to the experimental results, RHSBoost appears to be an attractive classification model for imbalance data.

Suggested Citation

  • Gong, Joonho & Kim, Hyunjoong, 2017. "RHSBoost: Improving classification performance in imbalance data," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 1-13.
    • Handle: RePEc:eee:csdana:v:111:y:2017:i:c:p:1-13
    DOI: 10.1016/j.csda.2017.01.005
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    References listed on IDEAS

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    1. Alfaro, Esteban & Gamez, Matias & García, Noelia, 2013. "adabag: An R Package for Classification with Boosting and Bagging," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i02).
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    Cited by:

    1. Ying Zhou & Xia Lin & Guotai Chi & Peng Jin & Mengtong Li, 2024. "EWT‐SMOTE to improve default prediction performance in imbalanced data: Analysis of Chinese data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 615-643, April.
    2. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    3. Khyati Ahlawat & Anuradha Chug & Amit Prakash Singh, 2019. "Benchmarking framework for class imbalance problem using novel sampling approach for big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 824-835, August.
    4. Florian Dumpert & Martin Beck, 2017. "Einsatz von Machine-Learning-Verfahren in amtlichen Unternehmensstatistiken [Use of machine learning in official business statistics]," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(2), pages 83-106, October.

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