RHSBoost: Improving classification performance in imbalance data

Joonho Gong, Hyunjoong Kim

Research output: Contribution to journalArticlepeer-review

87 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalComputational Statistics and Data Analysis
Publication statusPublished - 2017 Jul 1

Bibliographical note

Publisher Copyright:
© 2017 Elsevier B.V.

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics


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