Fuzzy Cognitive Maps

Also known as MAP

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Latest papers

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

Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
Sep 30, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data Generation Synthetic Tabular Data Generation
Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Sep 28, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data Generation Synthetic Tabular Data Generation
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 KoskoCausal World Models Fuzzy Cognitive Maps
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
LLM for the development of FCM
Jul 6, 2026Alexis KafantarisFuzzy Cognitive Maps
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 KoskoComputational Cognitive Modeling Bayesian Inference
University of Southern California, Los Angeles, CA 90007, USA · Florida International University, Miami, FL 33199, USA
Attractor FCM
Apr 30, 2026Alexis KafantarisRecurrent Neural Networks Physics-Informed ML
Non-monotonic causal discovery with Kolmogorov-Arnold Fuzzy Cognitive Maps
Apr 6, 2026Jose L. SalmeronFuzzy Cognitive Maps Fuzzy Logic
School of Engineering, CUNEF Universidad, Madrid, Spain
Looking Through Glass Box
Mar 6, 2026Alexis KafantarisFuzzy Cognitive Maps Fuzzy Logic
Athens University of Economics and Business

Sep 30, 2026J/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

Synthetic Data Generation Synthetic Tabular Data Generation Synthetic Clinical Data Generation Privacy-Preserving ML Fuzzy Cognitive Maps

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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.