Research Article
Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism
Table 3
The FRR and FAR of HVM-SVM compared with SVM, NN, and KNN (only list parts of the results).
| A total of 10000 records | 5000 for training 5000 for testing | 2000 for training 8000 for testing | 1000 for training 9000 for testing | 500 for training 9500 for testing | 100 for training 9900 for testing |
| SVM | FRR (fault ) | 0.9990301 | 0.9981768 | 0.9902465 | 0.9897119 | 0.006655164 | FAR () | 0.00048497 | 0.00060772 | 0.005689515 | 0.00617284 | 0.9933448 | FAR () | 0.0004849 | 0.00121544 | 0.004063939 | 0.004115226 | 0 |
| HVM-SVM | FRR (fault ) | 1 | 0.9990884 | 0.9929558 | 0.9927984 | 0.6958344 | FAR () | 0 | 0.00091158 | 0.00270929 | 0.00308642 | 0.3041653 | FAR () | 0 | 0 | 0.00379301 | 0.00360082 | 0 |
| NN | FRR (fault ) | 0.8962306 | 0.9541473 | 0.9788487 | 0.9405237 | 0.6340591 | FAR () | 0.1037694 | 0.04585265 | 0.0111413 | 0.01945227 | 0.1875932 | FAR () | 0 | 0 | 0.01001 | 0.040024 | 0.1783477 |
| KNN | FRR (fault ) | 0.9960396 | 0.9792714 | 0.9149528 | 0.8333333 | 0.4125697 | FAR () | 0.0039603 | 0.00188442 | 0.00166759 | 0.00157729 | 0.204764 | FAR () | 0 | 0.01884422 | 0.0794886 | 0.139327 | 0.382666 |
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