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

arXiv: https://arxiv.org/abs/1905.11226 · HTML: https://mlsanity.com/paper/1905.11226/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

## Built on by (most cited first, 2 of 2)

- [Explainability of Highly Associated Fuzzy Churn Patterns in Binary Classification](https://mlsanity.com/paper/2410.15827/) (2024)
- [Interpretable Hybrid Machine Learning Models Using FOLD-R++ and Answer Set Programming](https://mlsanity.com/paper/2506.19573/) (2025)

## Abstract (arXiv)

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.