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A Novel Boundary Oversampling Algorithm Based on Neighborhood Rough Set Model: NRSBoundary-SMOTE

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  • Feng Hu
  • Hang Li

Abstract

Rough set theory is a powerful mathematical tool introduced by Pawlak to deal with imprecise, uncertain, and vague information. The Neighborhood-Based Rough Set Model expands the rough set theory; it could divide the dataset into three parts. And the boundary region indicates that the majority class samples and the minority class samples are overlapped. On the basis of what we know about the distribution of original dataset, we only oversample the minority class samples, which are overlapped with the majority class samples, in the boundary region. So, the NRSBoundary-SMOTE can expand the decision space for the minority class; meanwhile, it will shrink the decision space for the majority class. After conducting an experiment on four kinds of classifiers, NRSBoundary-SMOTE has higher accuracy than other methods when C4.5, CART, and KNN are used but it is worse than SMOTE on classifier SVM.

Suggested Citation

  • Feng Hu & Hang Li, 2013. "A Novel Boundary Oversampling Algorithm Based on Neighborhood Rough Set Model: NRSBoundary-SMOTE," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, November.
    • Handle: RePEc:hin:jnlmpe:694809
    DOI: 10.1155/2013/694809
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    Cited by:

    1. Kim, Jongwoo & Kim, Hongil & Geum, Youngjung, 2023. "How to succeed in the market? Predicting startup success using a machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Winston Wang & Tun-Wen Pai, 2023. "Enhancing Small Tabular Clinical Trial Dataset through Hybrid Data Augmentation: Combining SMOTE and WCGAN-GP," Data, MDPI, vol. 8(9), pages 1-20, August.

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