| The topic is interesting.
On slide No.9, you mentioned about 18 call variables/features, what are they?
Features in the blue box on the right hand-side contains only 7+2 features.
So, finally in your CART model, you use these 7 features? 9 features?
Moreover, when comparing with the existing work for example [Hiuchen Li et. al., 2018], could you improve the detection accuracy?
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Those listed in the blue box are examples of main features selected. 18 features were considered for the CART model: Account Number, Server Number, Dialling Number, Dialled Number, Call Date, Call Duration, Call Time Plus Duration, Total Charges, Lead Dialled Number, Grouped Consecutive Dialled Number, Number of Calls Dialled Daily, Number of Calls per Group Daily, Daily Average Number of Calls To Destination, Daily Average Revenue to Destination,Daily Average Minutes of Use To Destination, Week Day, Time of Day, and TOS Category, as listed in Section IV of the paper.
We expect that accuracy can be improved by resampling the training/testing data or by including features that were not considered in this development |
