Fuzzy Cognitive Map

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4 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
Sep 30, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Tabular Data Clinical Decision Support
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece · Department of Computer Science and Biomedical Informatics University of Thessaly Lamia, Greece
Training-Free Clinical Reasoning through Medical Ontologies and Cognitive Mapping: A Symbolic-Probabilistic Knowledge Graph Framework
Sep 28, 2026Surajit DasClinical Prediction Biomedical Knowledge Graph
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Sep 28, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Tabular Data Fuzzy
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Converting Sequenced Fuzzy Cognitive Maps to Causal Virtual Worlds with Large Video Generators
Sep 14, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoFuzzy Cognitive Map
Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA · School of Computing and Information Sciences, Florida International University Miami, FL 33199, USA
When Is a Steerable Concept Representation Real? Measurement Confounds in a Cross-Family Audit of Neuroscience Parallels in LLMs
Aug 8, 2026Yuqi Wu, Shengming Zhao, Jie ChenLinear Activation Steering Neuroscience
College of Biomedical Engineering, Fudan University
Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences
Jul 14, 2026Arash Nikzad, Sasan Sarbishegi, Ali Dasmeh +8Fuzzy Cognitive Map Class Activation Mapping
Goethe University Frankfurt, Frankfurt, Germany · Independent Researcher, Teheran, Iran · Max Planck Institute for Human Development, Berlin, Germany +8
LLM for the development of FCM
Jul 6, 2026Alexis KafantarisFuzzy Cognitive Map Large Language Model Evaluation
OmniView-Space: Reinforcing Spatial Reasoning via Multi-Perspective Spatial Mapping
Jul 1, 2026Xudong Li, Mengdan Zhang, Peixian Chen +7Spatial Reasoning Multimodal Reasoning
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China · Tencent Youtu Lab · Beijing Institute of Technology
Agentic Collaborative Cognition for Zero-Shot 3D Understanding
Jun 23, 2026Wenxin Wang, Bo Zhang, Feng Chen +43D Scene Understanding Fuzzy Cognitive Map
Sichuan University · Adelaide University · University of Electronic Science and Technology of China +1
CoCoSI: Collaborative Cognitive Map Construction for Spatial Intelligence
Jun 9, 2026Yiming Zhang, Ruoxuan Cao, Zhihang ZhongFuzzy Cognitive Map Spatial Memory
2Cornell University · 1Shanghai Jiao Tong University
Hyperbolic Neural Population Geometry Benefits Computation
Jun 8, 2026Dennis Wu, Yi-Chun Hung, Braden Yuille +2Hopfield Networks Neural Circuits
Department of Computer Science, Northwestern University · Center for Foundation Models and Generative AI, Northwestern University · Integrated Science Program, Northwestern University +3
OVO-S-Bench: A Hierarchical Benchmark for Streaming Spatial Intelligence in Multimodal LLMs
Jun 2, 2026Yifei Li, Pengyiang Liu, Yuhang Zang +4Stable Spatial Understanding Multimodal Large Language Models
Tsinghua University · Shanghai AI Laboratory · Beihang University
Active Exploring like a Pigeon: Reinforcing Spatial Reasoning via Agentic Vision-Language Models
Jun 1, 2026Wei Deng, Xianlin Zhang, Mengshi QiSpatial Reasoning Fuzzy Cognitive Map
State Key Laboratory of Networking and Switching Technology · Beijing University of Posts and Telecommunications, China · School of Digital Media & Design Arts
Do LLMs Build World Models From Text? A Multilingual Diagnostic of Spatial Reasoning
May 27, 2026Zhikai Pan, Chih-Ting Liao, Chunrui Liu +5LLM Reasoning Strategies Spatial Reasoning
University of New South Wales · Essential Energy · University of Alabama at Birmingham +3
Exploratory Experience Shapes the Geometry of Predictive Representations
May 27, 2026Kseniia Shilova, Abdelrahman Sharafeldin, Advay Balakrishnan +1Next-State Prediction Stochastic Exploration
School of Mathematics Georgia Institute of Technology Atlanta, GA 30332 · College of Computing Georgia Institute of Technology Atlanta, GA 30332
MCNav: Memory-Aware Dynamic Cognitive Map for Zero-shot Goal-oriented Navigation
May 19, 2026Jingyu Li, Zhe Liu, Wenxiao Wu +1Object Goal Navigation Vision-Language Navigation
Fudan University · Shanghai Innovation Institute · University of Hong Kong +1
Agentic Chunking and Bayesian De-chunking of AI Generated Fuzzy Cognitive Maps: A Model of the Thucydides Trap
May 18, 2026Akash Kumar Panda, Olaoluwa Adigun, Bart KoskoFuzzy Cognitive Map Reasoning Graphs
University of Southern California, Los Angeles, CA 90007, USA · Florida International University, Miami, FL 33199, USA
LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map
May 16, 2026Jinzhou Tang, Sidi Liu, Waikit Xiu +2Fuzzy Cognitive Map Spatio-Temporal Reasoning
1Sun Yat-sen University
MAP: A Map-then-Act Paradigm for Long-Horizon Interactive Agent Reasoning
May 13, 2026Yuxin Liu, Ziang Ye, Yueqing Sun +6Fuzzy Cognitive Map Action-Conditioned Planning
University of Science and Technology of China · Meituan · Institution of Automation, Chinese Academy of Sciences +1
SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs
May 10, 2026Bo Gu, Zhikang Zhang, Zizhuang Wei +33D Spatial Reasoning Video Multimodal Large Language Models
1Fudan University · 2Huawei · 3Shenzhen Loop Area Institute
Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents
Apr 21, 2026Xu Chen, Shichao Xie, Zhining Gu +7Spatial Memory Embodied Agents
Amap, Alibaba Group, China.

Sep 30, 2026, cs.LGJ/K move · Enter open · S save

Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps

Michael Vasilakakis, Dimitris K. Iakovidis

Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece · Department of Computer Science and Biomedical Informatics University of Thessaly Lamia, Greece

Synthetic Tabular Data Clinical Decision Support Fuzzy Cognitive Map Performance Metric

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Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical data is restricted due to privacy, ethical, or data availability limitations. Existing probabilistic and deep generative models often lack interpretability and fail to preserve clinically meaningful dependencies, limiting their suitability for safety-critical applications. This paper proposes a novel application of Fuzzy Cognitive Maps (FCMs) in a framework for synthetic medical tabular data generation with explicit causality and privacy preservation. Clinical features are described using linguistically interpretable fuzzy sets, and inter-feature dependencies are encoded as FCM edge weights computed from fuzzy set intersections. Synthetic patient records are generated by propagating randomly initialized linguistic activation vectors through the FCM until convergence, followed by defuzzification to produce clinically coherent numerical values. The approach natively handles mixed data types, and domain constraints common in health records. Experimental evaluation on UCI medical benchmark datasets demonstrates competitive performance under a Train-on-Synthetic-Test-on-Real (TSTR) protocol. The proposed method achieves accuracy of up to 0.81 and AUROC of up to 0.90 on the Heart Disease dataset, matching or exceeding TVAE and Gaussian Copula baselines while running exclusively on CPU. Fidelity metrics including KS Complement (up to 0.91) and Correlation Similarity (up to 0.95) confirm strong statistical coherence, and DCR Baseline Protection scores consistently exceed those of TVAE, confirming adequate privacy guarantees. These results demonstrate that causally grounded, interpretable fuzzy modeling offers a computationally efficient and transparent alternative to deep generative models for trustworthy synthetic data generation in CBMSs.

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