How to handle conditional functions using SVM?

Question

My dataset contains functions that, if any, may have other related functions. To make an example:

Feature A: 0/1
Feature B: doesn't exist if A = 0, else: 1/-1
Feature C: doesn't exist if A = 0, else: 1/-1

These functions are not missing, they simply do not make sense if the parameter "A" is set to 0, so I can not use the imputation of the data. What is the best way to integrate these features into my dataset? The information is valuable, and if possible, I would not refuse it.

Answer 1

(, SVM), "0" . -1 +1 , , "0" , . , , , .