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Whitebox Induction of Default Rules Using High-Utility Itemset Mining

  • Farhad ShakerinEmail author
  • Gopal Gupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12007)

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.

Keywords

Inductive logic programming Machine learning Explainable AI Negation as failure Answer set programming Data mining 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.The University of Texas at DallasRichardsonUSA

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