Induction of Non-Monotonic Logic Programs to Explain Boosted Tree Models Using LIME

By Farhad Shakerin, Gopal Gupta. 2018

20 citations recorded by the source corpus.

We present a heuristic based algorithm to induce nonmonotonic logic programs that will explain the behavior of XGBoost trained classifiers. We use the technique based on the LIME approach to locally select the most important features contributing to the classification decision. Then, in order to explain the model’s global behavior, we propose the LIME-FOLD algorithm —a heuristic-based inductive logic programming (ILP) algorithm capable of learning nonmonotonic logic programs—that we apply to a transformed dataset produced by LIME. Our proposed approach is agnostic to the choice of the ILP algorithm. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics. Meanwhile, the number of induced rules dramatically decreases compared to ALEPH, a state-of-the-art ILP system.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Dimitris E. Simos
2. Yu Lei
3. D. Richard Kuhn
4. Raghu N. Kacker

Citations in the corpus

• Combinatorial Methods for Explainable AI (2020, methodology): “Consequently, there is a significant need for improvements in explainability of AI/ML system functions and decisions [1][2][3][4][5][6][7].”