White-box Induction From SVM Models: Explainable AI with Logic Programming. Issue 5 (September 2020)

• Record Type: Journal Article
• Authors: SHAKERIN, FARHAD; GUPTA, GOPAL
• Is Part Of: Theory and practice of logic programming. Volume 20:Issue 5(2020)
• Page Start: 656
• Page End: 670
• Publication Date: 2020-09
• Subjects: Explainable AI, -- Data Mining, -- Inductive Logic Programming, -- Machine Learning; Logic programming -- Periodicals; Artificial intelligence -- Computer programs -- Periodicals; Constraint programming (Computer science) -- Periodicals; 005.115
• DOI: 10.1017/S1471068420000356
• Languages: English
• ISSNs: 1471-0684

Abstract

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.