Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

Algorithm 1.

A run-down flow for trained model interpreting.

Input: a well-trained FCNN; the set of test instances ; parameter , the number of top important features of instance ; parameter , number of occurrences of feature as a percentage of total instances.

Output: decision feature set

  1. Initialization: ,

  2. For each do

  3. Compute the weight

  4. Get the top features with the highest weight

  5.

  6. End for

  7. For each feature f in

  8. Count the number of occurrences of feature f

  9. If

  10.

  11. End for

  12. Return