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Abstract Interpretation-Based Feature Importance for Support Vector Machines

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Verification, Model Checking, and Abstract Interpretation (VMCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14499))

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Abstract

We study how a symbolic representation for support vector machines (SVMs) specified by means of abstract interpretation can be exploited for: (1) enhancing the interpretability of SVMs through a novel feature importance measure, called abstract feature importance (AFI), that does not depend in any way on a given dataset or the accuracy of the SVM and is very fast to compute; and (2) certifying individual fairness of SVMs and producing concrete counterexamples when this verification fails. We implemented our methodology and we empirically showed its effectiveness on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels. Our experimental results prove that, independently of the accuracy of the SVM, our AFI measure correlates much strongly with stability of the SVM to feature perturbations than major feature importance measures available in machine learning software such as permutation feature importance, therefore providing better insight into the trustworthiness of SVMs.

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Notes

  1. 1.

    For multiple categorical features, we keep track of the relation between all the categorical features and their corresponding tiers through a global lookup table.

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Acknowledgements

Francesco Ranzato and Marco Zanella were partially funded by the Italian MIUR, under the PRIN 2017 project no. 201784YSZ5. Francesco Ranzato was partially funded by: the Italian MUR, under the PRIN 2022 PNRR project no. P2022HXNSC; Meta (formerly Facebook) Research, under a “Probability and Programming Research Award” and under a WhatsApp Research Award on “Privacy-aware Program Analysis”; by an Amazon Research Award for “AWS Automated Reasoning”. Caterina Urban was partially funded by the French PEPR Intelligence Artificielle SAIF project (ANR-23-PEIA-0006).

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Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, UK

    Abhinandan Pal

  2. Dipartimento di Matematica, University of Padova, Padova, Italy

    Francesco Ranzato & Marco Zanella

  3. INRIA and Ecole Normale Supérieure, Université PSL, Paris, France

    Caterina Urban

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  1. Abhinandan Pal
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  2. Francesco Ranzato
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  4. Marco Zanella
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Corresponding author

Correspondence to Francesco Ranzato .

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Editors and Affiliations

  1. CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

    Rayna Dimitrova

  2. Tel Aviv University, Tel Aviv, Israel

    Ori Lahav

  3. New York University, New York, NY, USA

    Sebastian Wolff

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Pal, A., Ranzato, F., Urban, C., Zanella, M. (2024). Abstract Interpretation-Based Feature Importance for Support Vector Machines. In: Dimitrova, R., Lahav, O., Wolff, S. (eds) Verification, Model Checking, and Abstract Interpretation. VMCAI 2024. Lecture Notes in Computer Science, vol 14499. Springer, Cham. https://doi.org/10.1007/978-3-031-50524-9_2

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