Research Article
Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model
Table 15
Performance of various well data.
| Well | Classification model | RMSE | MAE | Accuracy (%) | Running time (s) |
| W1 | RF | 0.3326 | 0.1106 | 88.94 | 1.5167 | SVM | 0.2681 | 0.0719 | 92.81 | 4.5861 | BSA-RF | 0.3269 | 0.1069 | 89.31 | 1.8064 | DMSDL-BSA-RF | 0.2806 | 0.0788 | 92.13 | 2.3728 | DMSDL-QBSA-RF | 0.2449 | 0.0600 | 94.00 | 1.4663 |
| W2 | RF | 0.4219 | 0.1780 | 82.20 | 3.1579 | SVM | 0.2983 | 0.0890 | 91.10 | 4.2604 | BSA-RF | 0.3963 | 0.1571 | 84.29 | 1.2979 | DMSDL-BSA-RF | 0.2506 | 0.062827 | 93.72 | 1.6124 | DMSDL-QBSA-RF | 0.2399 | 0.0576 | 94.24 | 1.2287 |
| W3 | RF | 0.4028 | 0.1622 | 83.78 | 2.4971 | SVM | 0.2507 | 0.0628 | 93.72 | 2.1027 | BSA-RF | 0.3631 | 0.1318 | 86.81 | 1.3791 | DMSDL-BSA-RF | 0.2341 | 0.0548 | 94.52 | 0.3125 | DMSDL-QBSA-RF | 0.0519 | 0.0027 | 99.73 | 0.9513 |
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