Research Article
Prediction of Defective Software Modules Using Class Imbalance Learning
Table 4
Comparison on the basis of specificity values on eight datasets.
| Dataset | SVM | CBNN | NB | RF | LR | -NN | BBN | C4.5 | LSTSVM | WLSTSVM |
| CM1 | 0.9603 | 0.7148 | 0.8286 | 0.5883 | 0.5772 | 0.6836 | 0.5322 | 0.4274 | 0.6612 | 0.6684 | KC1 | 0.9745 | 0.7002 | 0.9405 | 0.6224 | 0.5818 | 0.7082 | 0.6501 | 0.5301 | 0.6435 | 0.8040 | PC1 | 0.8078 | 0.8342 | 0.9028 | 0.6917 | 0.6132 | 0.7773 | 0.7804 | 0.6022 | 0.9750 | 0.7946 | PC3 | 0.6004 | 0.7282 | 0.9066 | 0.7009 | 0.5569 | 0.5491 | 0.5845 | 0.4867 | 0.5742 | 0.6981 | PC4 | 0.8027 | 0.8136 | 0.8750 | 0.6173 | 0.4936 | 0.5518 | 0.6173 | 0.4682 | 0.6641 | 0.7180 | MC2 | 0.7556 | 0.4664 | 0.9108 | 0.6702 | 0.4080 | 0.5422 | 0.6867 | 0.3288 | 0.4655 | 0.5118 | KC2 | 0.7385 | 0.7684 | 0.8143 | 0.6875 | 0.5934 | 0.6702 | 0.6224 | 0.5304 | 0.7391 | 0.7773 | KC3 | 0.9164 | 0.7429 | 0.7976 | 0.5402 | 0.4127 | 0.6936 | 0.5873 | 0.4566 | 0.5506 | 0.5882 |
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