Improving Compactness and Reducing Ambiguity of CFIRE Rule-Based Explanations

• URL: http://arxiv.org/abs/2601.03776v1
• Date: Wed, 07 Jan 2026 10:13:40 GMT
• Title: Improving Compactness and Reducing Ambiguity of CFIRE Rule-Based Explanations
• Authors: Sebastian Müller, Tobias Schneider, Ruben Kemna, Vanessa Toborek,
• Abstract summary: CFIRE is a recent algorithm that constructs compact surrogate rule models from local explanations.<n>We investigate this ambiguity and propose a post-hoc pruning strategy that removes rules with low contribution or conflicting coverage.<n> Experiments across multiple datasets confirm these improvements with minimal impact on predictive performance.
• Score: 2.522406590703041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Models trained on tabular data are widely used in sensitive domains, increasing the demand for explanation methods to meet transparency needs. CFIRE is a recent algorithm in this domain that constructs compact surrogate rule models from local explanations. While effective, CFIRE may assign rules associated with different classes to the same sample, introducing ambiguity. We investigate this ambiguity and propose a post-hoc pruning strategy that removes rules with low contribution or conflicting coverage, yielding smaller and less ambiguous models while preserving fidelity. Experiments across multiple datasets confirm these improvements with minimal impact on predictive performance.

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