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IEEE/ICACT20220035 Question.4 Questioner: vasaka.vis@mahidol.ac.th 2022-02-15 오후 10:27:23	IEEE/ICACT20220035 Answer.4 Answer by Auhor nishat.mowla@ri.se 2022-02-15 오후 10:27:23
Thank you for your presentation. It is an interesting topic. I have a few questions. (1) It is my first time to hear about "Explainable model", but when I see your Slide 7 "System Model (2/2)", it is just the normal random forest algorithm. Which part in this slide that represents the explianable model? (2) Could you extract the same amount of features from both SAD dataset and ORNL dataset? If yes, why the average training time of them are different? Based on your graphs in Figure 3 (Slide 12), for 113 features, the training time of ORNL dataset is almost two times of SAD dataset. Thank you	1) As shown in slide 6 (System model 1/2), the explainable model has two parts: feature exploration and sub-model exploration inspired by the concept of 'exploration as explanation' where explanations are generated from the random forest models used. This generated most contributing features and most contributing sub-models to provide explainability of the performance. 2) Similar feature format was used for both the datasets but the ORNL dataset had a higher number of data points for which it's training time is higher than the SAD dataset.

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