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UnimNeuron

2026-06Documento de conferência Acesso aberto
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dc.contributor.authorde Campos Souza, Paulo Vitor
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.date.accessioned2026-07-01T11:13:01Z
dc.date.available2026-07-01T11:13:01Z
dc.date.issued2026-06
dc.descriptionde Campos Souza, P. V. (2026). UnimNeuron: Automatic Connective Selection for Interpretable Neuro-Fuzzy Rules. In 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Proceedings (IEEE International Fuzzy Systems Conference Proceedings). IEEE Computer Society. https://doi.org/10.1109/FUZZ69877.2026.11626369
dc.description.abstractIn evolving data streams, neuro-fuzzy systems must adapt to non-stationary distributions while preserving interpretability and numerical stability. However, most evolving fuzzy models rely on fixed logical connectives or implicitly encode logical behavior, limiting transparency and flexibility. This paper introduces the UnimNeuron, a neuro-fuzzy neuron whose antecedent connective is automatically selected from data using an order-invariant unimum-based aggregation. The proposed neuron dynamically exhibits conjunctive (AND), disjunctive (OR), or compensatory (COMP) behavior without operator search or manual tuning, and provides an explicit index to inspect the logical regime of each rule. Based on this formulation, two evolving neuro-fuzzy classifiers are proposed and evaluated under a prequential protocol on real and synthetic data streams with concept drift. Experimental results show that the proposed models achieve predictive performance statistically comparable to state-of-the-art evolving fuzzy systems, while maintaining a compact rule base, zero numerical instabilities, and interpretable fuzzy rules. An ablation study further confirms the relevance of automatic connective selection across diverse scenarios.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent501320
dc.identifier.doi10.1109/FUZZ69877.2026.11626369
dc.identifier.isbn979-8-3315-5147-6
dc.identifier.issn1558-4739
dc.identifier.otherPURE: 171549404
dc.identifier.otherPURE UUID: bd81958d-c3fe-457b-9c5a-ddb919e30675
dc.identifier.otherScopus: 105047259661
dc.identifier.otherORCID: /0000-0002-7343-5844/work/219405481
dc.identifier.urihttp://hdl.handle.net/10362/204248
dc.identifier.urlhttps://www.scopus.com/pages/publications/105047259661
dc.identifier.urlhttps://linklings.s3.amazonaws.com/organizations/WCCI/wcci2026/submissions/stype108/SLRxV-fuzz_pap124s2.pdf
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Computer Society
dc.subjectFuzzy logic
dc.subjectneuro-fuzzy systems
dc.subjectuninorms
dc.subjectinterpretability
dc.subjectaggregation operators
dc.subjectControl and Optimization
dc.subjectLogic
dc.subjectModelling and Simulation
dc.titleUnimNeuronen
dc.title.subtitleAutomatic Connective Selection for Interpretable Neuro-Fuzzy Rulesen
dc.typeconference object
degois.publication.title2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) Proceedings
degois.publication.title2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
dspace.entity.typePublication
rcaap.rightsopenAccess

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