Research Article

One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

Table 4

Comparison of performance in “German” dataset with MAR type MVs.

Missing levelEvaluation criteriaLMFODCEMKI-SVMMS-SVMEM-SVMRI-SVM

%Total accuracy0.7200 (3)0.7367 (1)0.7033 (6)0.7167 (4)0.7220 (2)0.7148 (5)
AUC0.8724 (3)0.8789 (1)0.8447 (6)0.8662 (4)0.8732 (2)0.8653 (5)
Type I accuracy0.6167 (3)0.6411 (1)0.5979 (6)0.6117 (4)0.5980 (5)0.6209 (2)
Type II accuracy0.7643 (3)0.7777 (1)0.7485 (6)0.7617 (4)0.7752 (2)0.7550 (5)
%Total accuracy0.6730 (6)0.7205 (1)0.6777 (5)0.6990 (4)0.7000 (3)0.7107 (2)
AUC0.8300 (5)0.8661 (1)0.8054 (6)0.8318 (4)0.8334 (3)0.8592 (2)
Type I accuracy0.5833 (4)0.6012 (2)0.5615 (6)0.5938 (3)0.5705 (5)0.6310 (1)
Type II accuracy0.7114 (6)0.7606 (1)0.7275 (5)0.7441 (4)0.7555 (2)0.7448 (3)
%Total accuracy0.6580 (5)0.7057 (1)0.6583 (4)0.6553 (6)0.6750 (3)0.6850 (2)
AUC0.8215 (6)0.8533 (1)0.8230 (5)0.8390 (3)0.8484 (2)0.8317 (4)
Type I accuracy0.5380 (6)0.6070 (1)0.5446 (5)0.5534 (3)0.5459 (4)0.5683 (2)
Type II accuracy0.7094 (4)0.7480 (1)0.7071 (5)0.6990 (6)0.7303 (3)0.7350 (2)
%Total accuracy0.6331 (6)0.6759 (2)0.6467 (5)0.6531 (4)0.6772 (1)0.6600 (3)
AUC0.7990 (6)0.8350 (1)0.8071 (5)0.8159 (4)0.8233 (2)0.8213 (3)
Type I accuracy0.5063 (6)0.5826 (1)0.5217 (5)0.5467 (3)0.5447 (4)0.5542 (2)
Type II accuracy0.6874 (6)0.7159 (2)0.7002 (4)0.6987 (5)0.7340 (1)0.7053 (3)
%Total accuracy0.6050 (6)0.6610 (1)0.6290 (4)0.6150 (5)0.6550 (2)0.6467 (3)
AUC0.7818 (6)0.8211 (1)0.7910 (5)0.7945 (4)0.8109 (2)0.8046 (3)
Type I accuracy0.4627 (6)0.5466 (1)0.4934 (4)0.4658 (5)0.5187 (3)0.5249 (2)
Type II accuracy0.6660 (6)0.7100 (2)0.6871 (4)0.6789 (5)0.7134 (1)0.6989 (3)

Average rank5.101.205.054.202.602.85

Note: the bold-face in Table 4 shows the maximum of each row. The numbers in parentheses are the ranks of the six models with the corresponding evaluation criterion in each row.