Rule-Based Classification

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21 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Kolmogorov-Arnold Classifier Systems as Universal Approximators
Sep 29, 2026Hiroki Shiraishi, Hisao Ishibuchi, Masaya NakataUniversal Approximation Function Approximation
Graduate School of Engineering Science, Yokohama National University, Yokohama 240-8501, Japan · Digital Healthcare Research Department, Hitachi, Ltd., Tokyo 185-8601, Japan · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China +1
HyDI: A hybrid Deep Learning-Inductive Logic Programming ensemble for multi-label classification
Sep 29, 2026Simon Flügel, Till MossakowskiMulti-Label Classification Explainable Artificial Intelligence
Institute for Computer Science, University of Osnabrück, Neuer Graben 29, 49074 Osnabrück, Germany
Pair-Level Essay-Scale Republication and Reuse from Fragmented Historical Text Reuse: A Workflow Study on Eighteenth-Century Books and Newspapers
Aug 27, 2026Ke Shu, Kira Hinderks, Eetu Mäkelä +1Rule-Based Classification Historical Document Analysis
University of Helsinki Helsinki, Finland
Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles
Aug 16, 2026Srikumar KrishnamoorthyRule-Based Classification Interpretable ML
Information Systems Area, Indian Institute of Management Ahmedabad, India
Evaluating LLM-Generated Rules for Heart Disease Prediction
Aug 11, 2026Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal +2Rule-Based Classification Clinical Risk Prediction
Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, India · Department of Computer Science, Amity University Dubai, Dubai, UAE · College of Computing and Informatics, University of Sharjah,Sharjah, UAE
How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making
Aug 10, 2026Adia Lumadjeng, Ilker Birbil, Erman AcarRule-Based Classification Interpretable ML
University of Amsterdam Amsterdam, the Netherlands
Evidential Rule Learning for Interpretable Classification with Abstention
Aug 6, 2026Javier Fumanal-Idocin, Javier Andreu-PerezSelective Prediction Interpretable ML
School of Computer Science and Electronic Engineering University of Essex, Colchester CO4 3SQ, UK
Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification
Jul 12, 2026Siyu Wang, Wei Tan, Lulu ChenHierarchical Classification Retrieval-Augmented Classification
aGusu Laboratory of Materials, Suzhou, China · cSuzhou Digital China Wuxin Intelligent Technology Co., Ltd., Suzhou, China
Agentic generation of verifiable rules for deterministic, self-expanding reaction classification
Jul 1, 2026Daniel Armstrong, Maarten Dobbelaere, Valentas Olikauskas +4Rule-Based Classification
1École Polytechnique Fédérale de Lausanne (EPFL), Switzerland · 2Ghent University, Belgium · 3National Centre of Competence in Research (NCCR) Catalysis, Switzerland
RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules
Jul 1, 2026Ádám Kovács, Nadia Verdha, Gábor RecskiLLM Grounding LLM-Based Program Synthesis
KR Labs
Learning High Coverage Discriminative Parsimonious Rulesets
Jun 12, 2026Mariamma Antony, Raman Sankaran, Chiranjib Bhattacharyya +1Interpretable ML Rule-Based Classification
Indian Institute of Science, CV Raman Rd, Bengaluru, Karnataka 560012 · Compass, Bengaluru, Karnataka 560012
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
Jun 9, 2026Junli Zha, Jinbo Wang, Chao Zhou +1LLM Decision-Making Iterative Refinement
SF Express
Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling
Jun 1, 2026Vladimir BeskorovainyiHuman-in-the-Loop Annotation Text Classification
Besk Tech
FlagGAM: Rule-Basis Generalized Additive Models for Explainable Tabular Prediction
May 29, 2026Zijie Zhao, Roy E. WelschInterpretable ML Corruption Robustness
EECS Department, Massachusetts Institute of Technology, Cambridge, MA, USA · Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA
A Deterministic Agentic Workflow for HS Tariff Classification: Multi-Dimensional Rule Reasoning with Interpretable Decisions
May 14, 2026Yu Zhang, Dongjiang Zhuang, Qu Zhou +4Agentic Workflows Classification
School of Information and Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China · Department of Science and Technology (Shanghai), General Administration of Customs of the P.R.C. (GACC), Shanghai, China · Nanjing Jiyun Information Technology Co., Ltd., Nanjing, China +2
Optimal Pattern Detection Tree for Symbolic Rule-Based Classification
May 14, 2026Young-Chae Hong, Yangho ChenRule-Based Classification Interpretable ML
Amazon Seattle, USA
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Apr 24, 2026Tomáš Kalný, Martin Jureček, Mark StampConcept Drift Rule-Based Classification
Faculty of Information Technology Czech Technical University, Thakurova 9, Prague, Czech · Department of Computer Science, San Jose State University, San Jose, CA, USA
RLIE: Rule Generation with Logistic Regression, Iterative Refinement, and Evaluation for Large Language Models
Oct 22, 2025Yang Yang, Hua XU, Zhangyi Hu +1Logistic Regression Neuro-Symbolic Reasoning
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China · Institute of Deep Perception Technology, JITRI, Wuxi 214000, China
Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
May 30, 2025Ben Griffin, Aaron Ontoyin Yin, Diego Vidaurre +4Rule-Based Classification Rare-Event Prediction
University of Oxford, United Kingdom · Vela Research, San Francisco, United States · Aarhus University, Aarhus, Denmark +2
Probabilistic Truly Unordered Rule Sets
Jan 18, 2024Lincen Yang, Matthijs van LeeuwenInterpretable ML Multiclass Classification
LIACS, Leiden University Niels Bohrweg 1, 2333 CA, Leiden, The Netherlands
Distributionally Robust Regularization of Sparse Integer Programming Trained Learning Models
Nov 3, 2023Sanjeeb Dash, Soumyadip Ghosh, Joao Goncalves +1Rule-Based Classification Interpretable ML
IBM Research, Yorktown Heights, NY 10598

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Kolmogorov-Arnold Classifier Systems as Universal Approximators

Hiroki Shiraishi, Hisao Ishibuchi, Masaya Nakata

Graduate School of Engineering Science, Yokohama National University, Yokohama 240-8501, Japan · Digital Healthcare Research Department, Hitachi, Ltd., Tokyo 185-8601, Japan · Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China+1

Universal Approximation Function Approximation Rule-Based Classification Curse of Dimensionality

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As the input dimension

n

grows, rule-based machine learning, such as Learning Classifier Systems (LCSs), faces a fundamental scalability bottleneck for function approximation: both rule count and parameter count grow exponentially with

n

. Traditional LCSs partition the

n

-dimensional input space directly, requiring

\mathcal{O}(m^n)

rules for adequate coverage, where

m

is the per-variable resolution. This article breaks from this paradigm by reorganizing rules dimension-wise, guided by the Kolmogorov-Arnold representation theorem: any continuous

n

-dimensional function can be expressed as a finite superposition of one-dimensional functions. The proposed Kolmogorov-Arnold Classifier System (KACS) decomposes the target function into one-dimensional subproblems and assigns a dedicated ruleset to each, reducing the worst-case rule count from

\mathcal{O}(m^n)

\mathcal{O}(mn^2)

and replacing

n

-dimensional local models with one-dimensional models requiring only two parameters per rule, independent of

n

. We also provide the first constructive proof that an LCS, namely KACS, is a universal approximator for continuous functions on compact domains. Evaluated against a direct

n

-dimensional input space partitioning approach under otherwise identical conditions, KACS achieves competitive accuracy in many settings while using only 2% to 40% of the parameters. Our implementation is available at https://github.com/YNU-NakataLab/KACS.