RHSBoost: Improving classification performance in imbalance data

Joonho Gong and Hyunjoong Kim

Computational Statistics & Data Analysis, 2017, vol. 111, issue C, 1-13

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.

Keywords: Imbalanced data; AdaBoost; Ensemble; AUC; Undersampling; RHSBoost (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:111:y:2017:i:c:p:1-13

DOI: 10.1016/j.csda.2017.01.005

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