Research Article
[Retracted] An Effective Approach for Human Activity Classification Using Feature Fusion and Machine Learning Methods
Table 3
Classification results of experiment 2 along with the sensitivity and other measures.
| Weizmann | KTH | Method | (%) | (%) | (%) | (%) | (%) | (%) | (%) | (%) |
| Linear-SVM | 98.83 | 99.84 | 99.0 | 99.3 | 99.89 | 99.95 | 99.89 | 99.8 | Cubic-SVM | 99.34 | 99.89 | 99.5 | 99.5 | 98.81 | 99.94 | 98.60 | 99.7 | Complex tree | 85.40 | 97.15 | 85.0 | 88.3 | 98.32 | 99.67 | 97.37 | 98.5 | Fine-KNN | 87.26 | 97.21 | 95.4 | 91.1 | 99.42 | 99.84 | 99.38 | 99.2 | Subspace-KNN | 90.25 | 98.3 | 91.9 | 93.9 | 99.97 | 99.99 | 99.95 | 99.9 |
|
|