Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models
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.
Cite
@article{arxiv.1909.09017,
title = {Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models},
author = {Farhad Shakerin},
journal= {arXiv preprint arXiv:1909.09017},
year = {2019}
}
Comments
In Proceedings ICLP 2019, arXiv:1909.07646. arXiv admin note: substantial text overlap with arXiv:1808.00629, arXiv:1905.11226, arXiv:1802.06462, arXiv:1707.02693