White-box Induction From SVM Models: Explainable AI with Logic Programming
This addresses the issue of local optima in ILP for explainable AI, offering a method to better capture SVM logic, though it appears incremental as it builds on existing techniques.
The paper tackles the problem of inducing logic programs to explain SVM models by replacing data-dependent hill-climbing with a model-dependent search using support vectors and SHAP, resulting in outperforming other ILP algorithms in clause count and classification metrics.
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 a local optimum. 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 SVM model's underlying logic and outperforms %GG: the FOIL algorithm --> other ILP algorithms other ILP algorithms in terms of the number of induced clauses and classification evaluation metrics. This paper is under consideration for publication in the journal of "Theory and practice of logic programming".