Abstract
We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and training time compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.
Authors are partially supported by NSF Grants IIS 1718945 and IIS 1910131.
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Notes
- 1.
Full implementation is available at: https://github.com/fxs130430/SHAP_FOLD.
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Shakerin, F., Gupta, G. (2020). Whitebox Induction of Default Rules Using High-Utility Itemset Mining. In: Komendantskaya, E., Liu, Y. (eds) Practical Aspects of Declarative Languages. PADL 2020. Lecture Notes in Computer Science(), vol 12007. Springer, Cham. https://doi.org/10.1007/978-3-030-39197-3_11
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