Research Article
Detecting Suspects by Large-Scale Trajectory Patterns in the City
Table 3
Experimental results with different classification methods.
| Model | Methods | Accuracy | Precision | Recall | F3 measure |
| RF | SPM | 0.818 | 0.009 | 0.602 | 0.079 | LCM | 0.867 | 0.028 | 0.786 | 0.212 | TPM | 0.912 | 0.021 | 0.694 | 0.165 | LCM + TPM | 0.915 | 0.042 | 0.96 | 0.301 |
| NB | SPM | 0.852 | 0.007 | 0.714 | 0.064 | LCM | 0.861 | 0.018 | 0.726 | 0.147 | TPM | 0.923 | 0.015 | 0.857 | 0.114 | LCM + TPM | 0.935 | 0.014 | 0.818 | 0.121 |
| GBDT | SPM | 0.807 | 0.012 | 0.654 | 0.103 | LCM | 0.844 | 0.029 | 0.785 | 0.218 | TPM | 0.956 | 0.022 | 0.703 | 0.172 | LCM + TPM | 0.962 | 0.051 | 0.972 | 0.346 |
| LR | SPM | 0.844 | 0.009 | 0.651 | 0.08 | LCM | 0.851 | 0.013 | 0.673 | 0.111 | TPM | 0.892 | 0.018 | 0.664 | 0.145 | LCM + TPM | 0.906 | 0.019 | 0.886 | 0.159 |
| KNN | SPM | 0.837 | 0.014 | 0.71 | 0.119 | LCM | 0.852 | 0.034 | 0.71 | 0.238 | TPM | 0.869 | 0.015 | 0.69 | 0.125 | LCM + TPM | 0.950 | 0.035 | 0.86 | 0.256 |
| OSCVM | SPM | 0.907 | 0.018 | 0.912 | 0.153 | LCM | 0.912 | 0.019 | 0.995 | 0.162 | TPM | 0.909 | 0.018 | 0.932 | 0.154 | LCM + TPM | 0.921 | 0.017 | 0.930 | 0.146 |
| Deep learning | MST-CNN | 0.981 | 0.191 | 0.650 | 0.524 |
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