A random forests quantile classifier for class imbalanced data
作者：
Highlights：
• The new classifier jointly optimizes true positive and true negative rates for imbalanced data while simultaneously minimizing weighted risk.
• It outperforms the existing random forests method in complex settings of rare minority instances, high dimensionality and highly imbalanced data.
• Its performance is superior with respect to variable selection for imbalanced data.
• The classifier is also highly competitive for multiclass imbalanced data.
摘要
•The new classifier jointly optimizes true positive and true negative rates for imbalanced data while simultaneously minimizing weighted risk.•It outperforms the existing random forests method in complex settings of rare minority instances, high dimensionality and highly imbalanced data.•Its performance is superior with respect to variable selection for imbalanced data.•The classifier is also highly competitive for multiclass imbalanced data.
论文关键词：Weighted Bayes classifier,Response-based sampling,Class imbalance,Minority class,Random forests
论文评审过程：Received 21 June 2018, Revised 26 November 2018, Accepted 25 January 2019, Available online 29 January 2019, Version of Record 2 February 2019.
论文官网地址：https://doi.org/10.1016/j.patcog.2019.01.036
