Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining

Farhad Shakerin; Gopal Gupta

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arXiv · 1905.11226

Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining

Farhad Shakerin·Gopal Gupta

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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 running time of the training algorithm compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.

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Farhad Shakerin, Gopal Gupta. 2019-05-24. Induction of Non-Monotonic Rules From Statistical Learning Models Using High-Utility Itemset Mining. https://arxiv.org/abs/1905.11226

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