Compact Rule-Based Classifier Learning via Gradient Descent

• URL: http://arxiv.org/abs/2502.01375v2
• Date: Wed, 24 Sep 2025 09:43:29 GMT
• Title: Compact Rule-Based Classifier Learning via Gradient Descent
• Authors: Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez,
• Abstract summary: Fuzzy Rule-based Reasoner (FRR) is a gradient-based rule learning system.<n>FRR supports strict user constraints over rule-based complexity.<n>It achieves $96%$ of the accuracy of state-of-the-art additive rule-based models.
• Score: 2.564905016909138
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rule-based models are essential for high-stakes decision-making due to their transparency and interpretability, but their discrete nature creates challenges for optimization and scalability. In this work, we present the Fuzzy Rule-based Reasoner (FRR), a novel gradient-based rule learning system that supports strict user constraints over rule-based complexity while achieving competitive performance. To maximize interpretability, the FRR uses semantically meaningful fuzzy logic partitions, unattainable with existing neuro-fuzzy approaches, and sufficient (single-rule) decision-making, which avoids the combinatorial complexity of additive rule ensembles. Through extensive evaluation across 40 datasets, FRR demonstrates: (1) superior performance to traditional rule-based methods (e.g., $5\%$ average accuracy over RIPPER); (2) comparable accuracy to tree-based models (e.g., CART) using rule bases $90\%$ more compact; and (3) achieves $96\%$ of the accuracy of state-of-the-art additive rule-based models while using only sufficient rules and requiring only $3\%$ of their rule base size.

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