Abstract Interpretation-Based Feature Importance for SVMs

@article{Pal2022AbstractIF,
  title={Abstract Interpretation-Based Feature Importance for SVMs},
  author={Abhinanda Pal and Francesco Ranzato and Caterina Urban and Marco Zanella},
  journal={ArXiv},
  year={2022},
  volume={abs/2210.12456}
}
We propose a symbolic representation for support vector machines (SVMs) by means of abstract interpretation, a well-known and successful technique for designing and implementing static program analyses. We leverage this abstraction in two ways: (1) to enhance the interpretability of SVMs by deriving a novel feature importance measure, called abstract feature importance (AFI), that does not depend in any way on a given dataset of the accuracy of the SVM and is very fast to compute, and (2) for… 
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