Table 6 All studied models in form of acronyms along with the descriptions
Acronym | Description |
|---|---|
DT_9 | Decision tree algorithm with 9 predictor variables |
LR_9 | Logistic regression algorithm with 9 predictor variables |
S_DT_9 | Decision tree algorithm with 9 predictor variables, pre-processed by using the SMOTE |
S_LR_9 | Logistic regression algorithm with 9 predictor variables, pre-processed by using the SMOTE |
S_DT_10 | Decision tree algorithm with 10 predictor variables proposed by Endo et al. [10], pre-processed by using the SMOTE |
S_LR_10 | Logistic regression algorithm with 10 predictor variables proposed by Endo et al. [10], pre-processed by using the SMOTE |
S_DT_16 | Decision tree algorithm with 16 predictor variables proposed by Delen et al. [8], pre-processed by using the SMOTE |
S_LR_16 | Logistic regression algorithm with 16 predictor variables proposed by Delen et al. [8], pre-processed by using the SMOTE |
S_DT_20 | Decision tree algorithm with 20 predictor variables, pre-processed by using the SMOTE |
S_LR_20 | Logistic regression algorithm with 20 predictor variables, pre-processed by using the SMOTE |
S_pDT | Pruning decision tree algorithm pre-processed by using the SMOTE |
S_rLR | Logistic regression algorithm pre-processed by using the SMOTE (This model is constructed by the same predictor variables as in S_pDT) |
C_DT_9 | Decision tree algorithm with 9 predictor variables, wrapped with CSC |
C_LR_9 | Logistic regression algorithm with 9 predictor variables, wrapped with CSC |
C_DT_10 | Decision tree algorithm with 10 predictor variables proposed by Endo et al. [10], wrapped with CSC |
C_LR_10 | Logistic regression algorithm with 10 predictor variables proposed by Endo et al. [10], wrapped with CSC |
C_DT_16 | Decision tree algorithm with 16 predictor variables proposed by Delen et al. [8], wrapped with CSC |
C_LR_16 | Logistic regression algorithm with 16 predictor variables proposed by Delen et al. [8], wrapped with CSC |
C_DT_20 | Decision tree algorithm with 20 predictor variables, wrapped with CSC |
C_LR_20 | Logistic regression algorithm with 20 predictor variables, wrapped with CSC |
C_pDT | Pruning decision tree algorithm wrapped with CSC |
C_rLR | Logistic regression algorithm wrapped with CSC (This model is constructed by the same predictor variables as in C_pDT) |
U_DT_9 | Decision tree algorithm with 9 predictor variables, pre-processed by using the under-sampling approach |
U_LR_9 | Logistic regression algorithm with 9 predictor variables, pre-processed by using the under-sampling approach |
U_DT_10 | Decision tree algorithm with 10 predictor variables proposed by Endo et al. [10], pre-processed by using the under-sampling approach |
U_LR_10 | Logistic regression algorithm with 10 predictor variables proposed by Endo et al. [10], pre-processed by using the under-sampling approach |
U_DT_16 | Decision tree algorithm with 16 predictor variables proposed by Delen et al. [8], pre-processed by using the under-sampling approach |
U_LR_16 | Logistic regression algorithm with 16 predictor variables proposed by Delen et al. [8], pre-processed by using the under-sampling approach |
U_DT_20 | Decision tree algorithm with 20 predictor variables, pre-processed by using the under-sampling approach |
U_LR_20 | Logistic regression algorithm with 20 predictor variables, pre-processed by using the under-sampling approach |
U_pDT | Pruning decision tree algorithm pre-processed by using the under-sampling approach |
U_rLR | Logistic regression algorithm pre-processed by using the under-sampling approach (This model is constructed by the same predictor variables as in U_pDT) |
Ba_DT_9 | Decision tree algorithm with 9 predictor variables, combined with bagging |
Ba_LR_9 | Logistic regression algorithm with 9 predictor variables, combined with bagging |
Ba_DT_10 | Decision tree algorithm with 10 predictor variables proposed by Endo et al. [10], combined with bagging |
Ba_LR_10 | Logistic regression algorithm with 10 predictor variables proposed by Endo et al. [10], combined with bagging |
Ba_DT_16 | Decision tree algorithm with 16 predictor variables proposed by Delen et al. [8], combined with bagging |
Ba_LR_16 | Logistic regression algorithm with 16 predictor variables proposed by Delen et al. [8], combined with bagging |
Ba_DT_20 | Decision tree algorithm with 20 predictor variables, combined with bagging |
Ba_LR_20 | Logistic regression algorithm with 20 predictor variables, combined with bagging |
Ba_pDT | Pruning decision tree algorithm combined with bagging |
Ba _rLR | Logistic regression algorithm combined with bagging (This model is constructed by the same predictor variables as in Ba_pDT) |
Ad_DT_9 | Decision tree algorithm with 9 predictor variables, combined with AdaboostM1 |
Ad_LR_9 | Logistic regression algorithm with 9 predictor variables, combined with AdaboostM1 |
Ad_DT_10 | Decision tree algorithm with 10 predictor variables proposed by Endo et al. [10], combined with AdaboostM1 |
Ad_LR_10 | Logistic regression algorithm with 10 predictor variables proposed by Endo et al. [10], combined with AdaboostM1 |
Ad_DT_16 | Decision tree algorithm with 16 predictor variables proposed by Delen et al. [8], combined with AdaboostM1 |
Ad_LR_16 | Logistic regression algorithm with 16 predictor variables proposed by Delen et al. [8], combined with AdaboostM1 |
Ad_DT_20 | Decision tree algorithm with 20 predictor variables, combined with AdaboostM1 |
Ad_LR_20 | Logistic regression algorithm with 20 predictor variables, combined with AdaboostM1 |
Ad_pDT | Pruning decision tree algorithm combined with AdaboostM1 |
Ad_rLR | Logistic regression algorithm combined with AdaboostM1 (This model is constructed by the same predictor variables as in Ad_pDT) |