Table 4 |
|||||||||||||||||
| Performance measures of data mining algorithm at different levels of significance over Antibodies dataset | |||||||||||||||||
| SIGNIFICANCE | p < 5 x 10-8 | p < 5 x 10-7 | p < 5 x 10-6 | p < 5 x 10-5 | |||||||||||||
| Algorithm | Acc. | Sp | Sn | AUC | Acc. | Sp | Sn | AUC | Acc. | Sp | Sn | AUC | Acc | Sp | Sn | AUC | Avg. |
| R. Forest | 90.0 | 93.0 | 90.0 | 0.96 | 90.0 | 91.0 | 90.0 | 0.97 | 92.0 | 94.0 | 92.0 | 0.96 | 94.0 | 96.0 | 94.0 | 0.97 | 93.3 |
| Bayes Net | 88.0 | 92.0 | 88.0 | 0.96 | 88.0 | 91.0 | 88.0 | 0.96 | 94.0 | 95.0 | 94.0 | 0.95 | 92.0 | 95.0 | 92.0 | 0.96 | 92.5 |
| Naïve Bayes | 88.0 | 94.0 | 88.0 | 0.96 | 88.0 | 94.0 | 88.0 | 0.96 | 88.0 | 94.0 | 88.0 | 0.96 | 88.0 | 94.0 | 88.0 | 0.96 | 91.5 |
| SVM | 80.0 | 86.6 | 80.0 | 0.86 | 86.0 | 89.9 | 86.0 | 0.89 | 94.0 | 96.6 | 97.0 | 0.95 | 96.0 | 96.9 | 96.0 | 0.96 | 90.7 |
| MLP | 80.0 | 89.8 | 80.0 | 0.91 | 86.0 | 89.9 | 86.0 | 0.96 | 94.0 | 96.6 | 94.0 | 0.99 | dnf | dnf | dnf | dnf | 90.2* |
| SLR | 84.0 | 91.6 | 84.0 | 0.89 | 86.0 | 83.2 | 86.0 | 0.92 | 90.0 | 93.5 | 90.0 | 0.97 | 92.0 | 95.0 | 92.0 | 0.96 | 90.1 |
| KNN | 82.0 | 90.7 | 82.0 | 0.92 | 84.0 | 88.7 | 84.0 | 0.94 | 86.0 | 91.2 | 86.0 | 0.95 | 92.0 | 96.4 | 92.0 | 0.95 | 89.4 |
| Logistic R. | 72.0 | 85.3 | 72.0 | 0.92 | 84.0 | 90.1 | 84.0 | 0.93 | 92.0 | 96.4 | 92.0 | 0.98 | 90.0 | 96.1 | 90.0 | 0.98 | 89.1 |
| M5P | 80.0 | 91.5 | 80.0 | 0.92 | 76.0 | 87.4 | 76.0 | 0.90 | 78.0 | 89.4 | 78.0 | 0.91 | 74.0 | 85.4 | 74.0 | 0.89 | 83.2 |
| Hyper Pipes | 64.0 | 83.6 | 64.0 | 0.90 | 72.0 | 84.9 | 72.0 | 0.90 | 80.0 | 87.5 | 80.0 | 0.92 | 80.0 | 87.1 | 80.0 | 0.93 | 81.3 |
| K star | 88.0 | 93.4 | 88.0 | 0.94 | 94.0 | 97.2 | 94.0 | 0.95 | 82.0 | 91.8 | 82.0 | 0.93 | 20.0 | 90.2 | 20.8 | 0.68 | 80.7 |
| J48 | 80.0 | 92.5 | 80.0 | 0.86 | 72.0 | 87.0 | 72.0 | 0.87 | 70.0 | 87.6 | 70.0 | 0.79 | 64.0 | 86.1 | 64.0 | 0.77 | 78.4 |
| ASC | 82.0 | 91.7 | 82.0 | 0.87 | 72.0 | 82.9 | 72.0 | 0.82 | 70.0 | 87.8 | 70.0 | 0.76 | 64.0 | 88.5 | 64.0 | 0.75 | 77.9 |
| Random Tree | 72.0 | 90.3 | 72.0 | 0.81 | 64.0 | 82.1 | 64.0 | 0.73 | 68.0 | 87.7 | 68.0 | 0.78 | 74.0 | 89.7 | 74.0 | 0.82 | 76.2 |
| VFI | 72.0 | 88.5 | 72.0 | 0.86 | 64.0 | 91.9 | 64.0 | 0.85 | 58.0 | 94.7 | 58.0 | 0.86 | 52.0 | 94.5 | 52.0 | 0.89 | 75.5 |
| LDA | 68.0 | 84.5 | 68.0 | 0.88 | 40.0 | 81.1 | 40.0 | 0.71 | 42.0 | 89.7 | 48.8 | 0.54 | 20.0 | 88.4 | 25.0 | 0.58 | 60.4 |
| K means | 46.0 | 68.7 | 46.0 | 0.57 | 46.0 | 68.7 | 46.0 | 0.57 | 40.0 | 68.1 | 40.0 | 0.54 | 40.0 | 68.1 | 40.0 | 0.54 | 52.5 |
Acc: Accuracy, Sp: Specificity, Sn: Sensitivity, AUC: Area under ROC curve, Avg: Average score in % for each algorithms, dnf: Did not Finish”, * denotes Avg. from 3 significance levels. Measures >90% are marked in bold.
Kukreja et al. BMC Bioinformatics 2012 13:139 doi:10.1186/1471-2105-13-139
