BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor
Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

TL;DR
Bellatrex is a novel method that extracts a small set of diverse, accurate rules to explain predictions of tree-ensemble models across various tasks, maintaining high fidelity and outperforming existing explainability methods.
Contribution
The paper introduces Bellatrex, a unified framework capable of explaining ensemble predictions with few rules across multiple learning tasks, a first in the field.
Findings
Bellatrex effectively explains predictions with few rules.
It maintains high fidelity to the original ensemble.
It outperforms other explainability methods in predictive accuracy.
Abstract
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a novel method that is Building Explanations through a LocalLy AccuraTe Rule EXtractor (Bellatrex), and is able to explain the forest prediction for a given test instance with only a few diverse rules. Starting from the decision trees generated by a random forest, our method 1) pre-selects a subset of the rules used to make the prediction, 2) creates a vector representation of such rules, 3) projects them to a low-dimensional space, 4) clusters such representations to pick a rule from each cluster to explain the instance prediction. We test the effectiveness of Bellatrex on 89 real-world datasets…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Data Stream Mining Techniques · Machine Learning and Data Classification
