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

2019/05/24 by Farhad Shakerin, Gopal Gupta, Shakerin, Farhad +1 · 1 citation
Computer Science · #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Logic in Computer Science (cs.LO) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.1905.11226

openalex publication_date 2019/05/24 · openalex created_date 2019/05/29 · openalex updated_date 2026/07/28

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.

Citations

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