 |
The Table 7 presents a comparative analysis of the performance
measures between RF and DNN classifiers, assessing their effectiveness in both two-class and multiclass scenarios.
In the two-class classification, RF outperforms DNN across various metrics.
RF achieves a higher accuracy (90.20%) compared to DNN (88.35%), and a superior F1-score (90.45%) compared to DNN (88.63%).
The precision of RF (91.98%) also exceeds that of DNN (90.93%). Moving to the multiclass setting, RF maintains its dominance, exhibiting a higher accuracy (75.17%), F1-score (72.93%), and precision (77.44%) compared to DNN (74.33%, 73.07%, and 77.86%, respectively).
These results emphasize the robust performance of RF in both two-class and multiclass classification scenarios, highlighting its efficacy in accurately classifying instances across various metrics. |
IEEE/ICACT20240459 Slide.16
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