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White-box Induction From SVM Models: Explainable AI with Logic Programming

Published online by Cambridge University Press:  21 September 2020

FARHAD SHAKERIN Open the ORCID record for FARHAD SHAKERIN [Opens in a new window]  and
GOPAL GUPTA
FARHAD SHAKERIN
Affiliation:
The University of Texas at Dallas, Texas, USA (e-mail: Farhad.Shakerin@utdallas.edu, Gopal.Gupta@utdallas.edu)
GOPAL GUPTA
Affiliation:
The University of Texas at Dallas, Texas, USA (e-mail: Farhad.Shakerin@utdallas.edu, Gopal.Gupta@utdallas.edu)
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Abstract

We focus on the problem of inducing logic programs that explain models learned by the support vector machine (SVM) algorithm. The top-down sequential covering inductive logic programming (ILP) algorithms (e.g., FOIL) apply hill-climbing search using heuristics from information theory. A major issue with this class of algorithms is getting stuck in local optima. In our new approach, however, the data-dependent hill-climbing search is replaced with a model-dependent search where a globally optimal SVM model is trained first, then the algorithm looks into support vectors as the most influential data points in the model, and induces a clause that would cover the support vector and points that are most similar to that support vector. Instead of defining a fixed hypothesis search space, our algorithm makes use of SHAP, an example-specific interpreter in explainable AI, to determine a relevant set of features. This approach yields an algorithm that captures the SVM model’s underlying logic and outperforms other ILP algorithms in terms of the number of induced clauses and classification evaluation metrics.

Keywords

Explainable AIData MiningInductive Logic ProgrammingMachine Learning
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 20 , Issue 5: 36th International Conference on Logic Programming Special Issue I , September 2020 , pp. 656 - 670
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Copyright
© The Author(s), 2020. Published by Cambridge University Press

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