Research Article
Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification
Table 4
EMG signal classification results for MultiBoosting.
| ā | Accuracy (%) | F-measure | ROC area | Kappa |
| ANN | 98.33 | 0.983 | 0.988 | 0.975 | k-NN | 91.33 | 0.913 | 0.976 | 0.87 | SVM | 95.88 | 0.959 | 0.979 | 0.9381 | RF | 98.79 | 0.988 | 0.998 | 0.9819 | C4.5 | 98.83 | 0.980 | 0.999 | 0.999 | Random Tree | 93.75 | 0.937 | 0.953 | 0.9063 | REPTree | 98.04 | 0.980 | 0.999 | 0.9706 | LADTree | 93.92 | 0.939 | 0.993 | 0.9088 | NB | 89.54 | 0.894 | 0.932 | 0.8431 |
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