A Fast Interpretable Fuzzy Tree Learner

• URL: http://arxiv.org/abs/2512.11616v1
• Date: Fri, 12 Dec 2025 14:51:07 GMT
• Title: A Fast Interpretable Fuzzy Tree Learner
• Authors: Javier Fumanal-Idocin, Raquel Fernandez-Peralta, Javier Andreu-Perez,
• Abstract summary: Fuzzy rule-based systems have been mostly used in interpretable decision-making.<n> interpretability requires both sensible linguistic partitions and small rule-base sizes.<n>We propose an adaptation of classical tree-based splitting algorithms from crisp rules to fuzzy trees.
• Score: 2.564905016909138
• License: http://creativecommons.org/licenses/by/4.0/
• 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

Related papers

• (Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification [1.6504157612470989]
  Deep neural networks are widely used in practical applications of AI, but their inner structure and complexity made them generally not easily interpretable.<n>In this work, a differentiable approximation of $L_0$ regularization is adapted into a logic-based neural network, the Multi-layer Logical Perceptron (MLLP), to study its efficacy in reducing the complexity of its discrete interpretable version, the Concept Rule Set (CRS)<n>The results are compared to alternatives like Random Binarization of the network weights, to determine if better results can be achieved when using a less-noisy technique that sparsifies the
  arXiv  Detail & Related papers  (2025-09-26T14:13:08Z)
• Provable Scaling Laws for the Test-Time Compute of Large Language Models [84.00141420901038]
  We propose two algorithms that enjoy provable scaling laws for the test-time compute of large language models.<n>One is a two-stage knockout-style algorithm, where each candidate is evaluated by its average win rate against multiple opponents.<n>The other is a two-stage league-style algorithm, where each candidate is evaluated by its average win rate against multiple opponents.
  arXiv  Detail & Related papers  (2024-11-29T05:29:47Z)
• Training Neural Networks as Recognizers of Formal Languages [87.06906286950438]
  We train and evaluate neural networks directly as binary classifiers of strings.<n>We provide results on a variety of languages across the Chomsky hierarchy for three neural architectures.<n>Our contributions will facilitate theoretically sound empirical testing of language recognition claims in future work.
  arXiv  Detail & Related papers  (2024-11-11T16:33:25Z)
• Explainable AI using expressive Boolean formulas [0.6323908398583082]
  We implement an interpretable machine classification model for Explainable AI (XAI) based on expressive Boolean formulas. Potential applications include credit scoring and diagnosis of medical conditions.
  arXiv  Detail & Related papers  (2023-06-06T19:18:46Z)
• The Cascaded Forward Algorithm for Neural Network Training [61.06444586991505]
  We propose a new learning framework for neural networks, namely Cascaded Forward (CaFo) algorithm, which does not rely on BP optimization as that in FF. Unlike FF, our framework directly outputs label distributions at each cascaded block, which does not require generation of additional negative samples. In our framework each block can be trained independently, so it can be easily deployed into parallel acceleration systems.
  arXiv  Detail & Related papers  (2023-03-17T02:01:11Z)
• A Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks [65.34977803841007]
  Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. We show how by simply changing the temporal scheduling of the update rule for the synaptic weights leads to an algorithm that is much more efficient and stable than the original one.
  arXiv  Detail & Related papers  (2022-11-16T00:11:04Z)
• Regularized impurity reduction: Accurate decision trees with complexity guarantees [20.170305081348328]
  We propose a tree-induction algorithm that gives a logarithmic approximation guarantee on the tree complexity. The enhanced algorithms strike an excellent balance between predictive accuracy and tree complexity.
  arXiv  Detail & Related papers  (2022-08-23T13:15:19Z)
• Efficient Learning of Interpretable Classification Rules [34.27987659227838]
  This paper contributes an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for classification rules expressible in proposition logic. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability.
  arXiv  Detail & Related papers  (2022-05-14T00:36:38Z)
• Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks [65.23508422635862]
  We propose learning rules with the recently proposed logical neural networks (LNN) Compared to others, LNNs offer strong connection to classical Boolean logic. Our experiments on standard benchmarking tasks confirm that LNN rules are highly interpretable.
  arXiv  Detail & Related papers  (2021-12-06T19:38:30Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• NeuroLogic Decoding: (Un)supervised Neural Text Generation with Predicate Logic Constraints [75.66980495245926]
  Conditional text generation often requires lexical constraints, i.e., which words should or shouldn't be included in the output text. We propose NeuroLogic Decoding, a simple yet effective algorithm that enables neural language models -- supervised or not -- to generate fluent text. Our results suggest the limit of large-scale neural networks for fine-grained controllable generation and the promise of inference-time algorithms.
  arXiv  Detail & Related papers  (2020-10-24T11:55:22Z)
• Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems [7.8140593450932965]
  We represent a classifier as an assembly of simple mathematical rules using a non-linear decision tree (NLDT) By restricting the structure of split-rule at each conditional node and depth of the decision tree, the interpretability of the classifier is assured. Results on two to 500-feature problems are encouraging and open up further scopes of applying the proposed approach to more challenging and complex classification tasks.
  arXiv  Detail & Related papers  (2020-08-02T06:35:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.