Table 4 Performances’ summary of algorithm-level methods
From: A survey on addressing high-class imbalance in big data
Technique | GMa | TP * TNb | AUCc | Ad | AGe | Ff | FAEg | W h | TPi | GDj | BDFk |
|---|---|---|---|---|---|---|---|---|---|---|---|
Cost-sensitive | |||||||||||
 Lopez et al. [9] | Apache Hadoop | ||||||||||
  Chi-FRBCS-Big DataCS | – | – | 0.99 | – | – | – | – | – | – | – | |
 Wang et al. [11] | – | ||||||||||
  CS-SSOL | – | – | – | 0.99 | – | – | – | – | – | – | |
Hybrid/ensemble | |||||||||||
 Marchant and Rubinstein [58] | – | ||||||||||
  OASIS | – | – | – | – | – | – | 10−5 | – | – | – | |
 Maurya [13] | – | ||||||||||
  IBO | – | – | – | – | – | – | – | 0.87 | – | – | |
 Veeramachaneni et al. [60] | – | ||||||||||
  AI2 | – | – | 0.85 | – | – | – | – | – | – | – | |
 Galpert et al. [14] | Apache Hadoop and Apache Spark | ||||||||||
  ROS + SVM-BD | 0.88 | – | 0.89 | – | – | – | – | – | – | – | |
 Wei et al. [64] | – | ||||||||||
  i-Alertor | – | – | – | – | – | – | – | – | 0.66 | – | |
 D’Addabbo and Maglietta [67] | – | ||||||||||
  PSS-SVM | – | – | – | – | – | 0.99 | – | – | – | – | |
 Triguero et al. [3] | Apache Hadoop | ||||||||||
  ROSEFW-RF | – | 0.53 | – | – | – | – | – | – | – | – | |
 Zhai et al. [70] | Apache Hadoop | ||||||||||
  ELM ensemble | 0.97 | – | – | – | – | – | – | – | – | – | |
 Hebert [72] | – | ||||||||||
  RF | – | – | – | – | – | – | – | – | – | 0.15 | |
  XGBoost | – | – | – | – | 0.05 | – | – | – | – | – | |
 Rio et al. [46] | Apache Hadoop | ||||||||||
  ROS | 0.99 | – | – | – | – | – | – | – | – | – | |
  RUS | 0.98 | – | – | – | – | – | – | – | – | – | |
  SMOTE | 0.91 | – | – | – | – | – | – | – | – | – | |
  RF | 0.97 | – | – | – | – | – | – | – | – | – | |
 Baughman et al. [74] | – | ||||||||||
  DeepQA | – | – | – | 0.28 | – | – | – | – | – | – | |