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Ensembling Imbalanced-Spatial-Structured Support Vector Machine

Author

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  • Liu, Xin
  • Yi, Grace Y.
  • Bauman, Glenn
  • He, Wenqing

Abstract

The support vector machine (SVM) and its extensions have been widely used in various areas. However, these methods cannot effectively handle imbalanced data with spatial association. The ensembling imbalanced-spatial-structured support vector machine (EISS-SVM) method is proposed to handle such data. Not only the proposed method accommodates the relationship between the response and predictors, but also accounts for the spatial correlation existing in data which may be imbalanced. The EISS-SVM classifier embraces the usual SVM as a special case. Numerical studies show satisfactory performance of the proposed method, and the analysis results are reported for the application of the proposed method to handling the imaging data from an ongoing prostate cancer research conducted in Canada.

Suggested Citation

  • Liu, Xin & Yi, Grace Y. & Bauman, Glenn & He, Wenqing, 2021. "Ensembling Imbalanced-Spatial-Structured Support Vector Machine," Econometrics and Statistics, Elsevier, vol. 17(C), pages 145-155.
    • Handle: RePEc:eee:ecosta:v:17:y:2021:i:c:p:145-155
    DOI: 10.1016/j.ecosta.2020.02.003
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    References listed on IDEAS

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    1. Haydemar Núñez & Luis Gonzalez-Abril & Cecilio Angulo, 2017. "Improving SVM Classification on Imbalanced Datasets by Introducing a New Bias," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 427-443, October.
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