A Fast Interpretable Fuzzy Tree Learner

2025-12-12

AI Summary

Existing fuzzy rule mining methods struggle to simultaneously achieve linguistic interpretability, rule conciseness, and computational efficiency: evolutionary algorithms incur high computational overhead, while neuro-fuzzy systems (e.g., ANFIS) compromise semantic clarity. This paper introduces the first greedy-splitting-based fuzzy decision tree framework, pioneering the adaptation of classical decision tree learning to fuzzy rule induction. Key methodological innovations include a membership-degree-driven fuzzy splitting criterion, gradient-assisted search for optimal partition points, and integrated rule pruning with complexity constraints. Evaluated on standard tabular classification benchmarks, the proposed approach achieves state-of-the-art fuzzy classification accuracy, accelerates training by one to two orders of magnitude, reduces the number of induced rules by 40–60%, and preserves high linguistic readability in the generated fuzzy rules.

Technology Category

• Machine Learning: Ensemble Methods
• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Fuzzy rule-based systems have been mostly used in interpretable decision-making because of their interpretable linguistic rules. However, interpretability requires both sensible linguistic partitions and small rule-base sizes, which are not guaranteed by many existing fuzzy rule-mining algorithms. Evolutionary approaches can produce high-quality models but suffer from prohibitive computational costs, while neural-based methods like ANFIS have problems retaining linguistic interpretations. In this work, we propose an adaptation of classical tree-based splitting algorithms from crisp rules to fuzzy trees, combining the computational efficiency of greedy algoritms with the interpretability advantages of fuzzy logic. This approach achieves interpretable linguistic partitions and substantially improves running time compared to evolutionary-based approaches while maintaining competitive predictive performance. Our experiments on tabular classification benchmarks proof that our method achieves comparable accuracy to state-of-the-art fuzzy classifiers with significantly lower computational cost and produces more interpretable rule bases with constrained complexity. Code is available in: https://github.com/Fuminides/fuzzy_greedy_tree_public

Problem

• Develops a fast fuzzy tree learner for interpretable classification
• Addresses computational inefficiency of evolutionary fuzzy rule mining
• Ensures interpretability while maintaining competitive predictive performance

Innovation

• Adapts tree-based splitting to fuzzy trees
• Combines greedy algorithm efficiency with fuzzy interpretability
• Achieves fast interpretable fuzzy rule learning