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From Formal Boosted Tree Explanations to Interpretable Rule Sets

Authors Jinqiang Yu , Alexey Ignatiev , Peter J. Stuckey

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Author Details

Jinqiang Yu
  • Department of Data Science and AI, Monash University, Clayton, Australia
  • Australian Research Council OPTIMA ITTC, Clayton, Australia
Alexey Ignatiev
  • Department of Data Science and AI, Monash University, Clayton, Australia
Peter J. Stuckey
  • Department of Data Science and AI, Monash University, Clayton, Australia
  • Australian Research Council OPTIMA ITTC, Clayton, Australia

Funding

This research was partially funded by the Australian Government through the Australian Research Council Industrial Transformation Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Project ID IC200100009.

Cite As Get BibTex

Jinqiang Yu, Alexey Ignatiev, and Peter J. Stuckey. From Formal Boosted Tree Explanations to Interpretable Rule Sets. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 38:1-38:21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.CP.2023.38

Abstract

The rapid rise of Artificial Intelligence (AI) and Machine Learning (ML) has invoked the need for explainable AI (XAI). One of the most prominent approaches to XAI is to train rule-based ML models, e.g. decision trees, lists and sets, that are deemed interpretable due to their transparent nature. Recent years have witnessed a large body of work in the area of constraints- and reasoning-based approaches to the inference of interpretable models, in particular decision sets (DSes). Despite being shown to outperform heuristic approaches in terms of accuracy, most of them suffer from scalability issues and often fail to handle large training data, in which case no solution is offered. Motivated by this limitation and the success of gradient boosted trees, we propose a novel anytime approach to producing DSes that are both accurate and interpretable. The approach makes use of the concept of a generalized formal explanation and builds on the recent advances in formal explainability of gradient boosted trees. Experimental results obtained on a wide range of datasets, demonstrate that our approach produces DSes that more accurate than those of the state-of-the-art algorithms and comparable with them in terms of explanation size.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Machine learning
Keywords
  • Decision set
  • interpretable model
  • gradient boosted tree
  • BT compilation

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