Table 11 Classification results of datasets after feature selection under SVC
From: Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
Imputation methods | FSM(No. of selected features) | Accuracy | F1 | AUC |
|---|---|---|---|---|
Mean | LASSO(20) | 0.7884(<e-33) | 0.8570(1.1093e-31) | 0.8338(< e-33) |
SCAD(41) | 0.7227(1.1093e-31) | 0.8211(1.2325e-32) | 0.7798(4.9303e-32) | |
BAR(7) | 0.6963(4.9303e-32) | 0.8209(1.2325e-32) | 0.7321(<e-33) | |
EM | LASSO(20) | 0.6963(4.9303e-32) | 0.8209(1.2325e-32) | 0.7408(4.9303e-32) |
SCAD(30) | 0.6964( 4.9304e-32) | 0.8210(1.2326e-32) | 0.7481(4.9304e-32) | |
BAR(7) | 0.6963(4.9303e-32) | 0.8209(1.2325e-32) | 0.7311(1.2326e-32) | |
KNN | LASSO(20) | 0.7574(1.2325e-32) | 0.8369(4.9303e-32) | 0.7959(4.9303e-32) |
SCAD(38) | 0.7648(4.9304e-32) | 0.8408(1.2325e-32) | 0.8082(1.1093e-31) | |
BAR(7) | 0.6963(4.9303e-32) | 0.8209(1.2325e-32) | 0.7311(1.2326e-32) | |
DAE | LASSO(27) | 0.7223(1.1093e-31) | 0.8157(<e-33) | 0.7845(<e-33) |
SCAD(62) | 0.7089( 1.2326e-32) | 0.8139(<e-33) | 0.7648(1.2325e-32) | |
BAR(7) | 0.6963(4.9303e-32) | 0.8209(1.2325e-32) | 0.6851(1.2325e-32) | |
GAIN | LASSO(10) | 0.7971(1.2326e-32) | 0.8524(<e-33) | 0.8650(4.9304e-32) |
SCAD(52) | 0.7940((4.9303e-32) | 0.8506(<e-33) | 0.8666(<e-33) | |
BAR(9) | 0.7980(1.2325e-32) | 0.8528(1.1093e-31) | 0.8664(4.9303e-32) |