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The average training time increases with the increasing number of features in both the datasets as expected. However, the training performance in terms of F1-score is not always increasing proportionately with an increasing number of features. In the case of our proposed model VGRF, with the SAD dataset, the highest F1-score is achieved at 113 features which have an average training time of 110s which is closer to the training time measured at 90 features (i.e., 95.03s). The ORNL dataset achieves the highest F1-score with 113 features at an average training time of 202:95s which is closer to the F1-score achieved with 90 features with an average training time of 142.51s. |
IEEE/ICACT20220035 Slide.12
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