eFCMG - An Evolving Fuzzy Classifier with Participatory Learning and Multivariable Gaussian for Data Stream

eFCMG - An Evolving Fuzzy Classifier with Participatory Learning and Multivariable Gaussian for Data Stream | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article eFCMG - An Evolving Fuzzy Classifier with Participatory Learning and Multivariable Gaussian for Data Stream Alisson Silva, Savio Rodrigues, Paulo Vitor de Campos Souza This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5566310/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper introduces a novel evolving fuzzy classifier that begins with no initial structure and develops incrementally through a participatory learning-based clustering algorithm. It employs multivariable Gaussian membership functions for rule antecedents and class outputs for consequents. The classifier's learning algorithm is designed to adjust dynamically by creating, merging, deleting, and updating clusters and rules. Uniquely, it features a 'procrastination' approach where clusters are initially formed in a disabled state to robustly manage outliers and ensure only representative data influence the model. Clusters are refined based on compatibility measures using the Mahalanobis distance, with adjustments to learning rates influenced by the nature of incoming data—slowing for anomalies and accelerating for typical inputs. This mechanism enhances adaptability and model accuracy, distinguishing it from existing fuzzy classifiers. Comparative analyses on binary and multiclass tasks demonstrate its superior or competitive performance, underscoring the classifier's innovative approach to evolving fuzzy classification. Artificial Intelligence and Machine Learning Evolving Fuzzy Systems Classifier Data Stream Participatory Learning Multivariable Gaussian Membership Function. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5566310","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":385289332,"identity":"e2009978-edfe-479b-8d09-9fdca409da2f","order_by":0,"name":"Alisson Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACdsYGZK4NEDM2HsCrhRlVSxpISwMBLajcw2ASrxb+Zubmjz8q7kTzzz68TeLDn/N2a9sPA22psYnGpUXiMGObNM+ZZ7kzzqWVSc5su5287UwiUMuxtNwGXHqAWpgZ2w7nNpzhMZPmbbidbHYAqIWx4TBOLfKHGZs//gRqmQ/S8ufPuWSz8w/xazE4zNggwQvUsgGkhYHtgJ3ZDQK2GEL8cjh34xm2YsvetuQEsxtAWxLw+EXuePtjYIgdzp13hnnjjR9/7OzNzqc/fPChxga395EdCSISwSoTiFAO12JPpOJRMApGwSgYQQAAXQZnUEOFZJMAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-1023-6514","institution":"CEFET-MG","correspondingAuthor":true,"prefix":"","firstName":"Alisson","middleName":"","lastName":"Silva","suffix":""},{"id":385290961,"identity":"f02db946-6235-40ae-89cf-d9290bfa78c0","order_by":1,"name":"Savio Rodrigues","email":"","orcid":"","institution":"CEFET-MG","correspondingAuthor":false,"prefix":"","firstName":"Savio","middleName":"","lastName":"Rodrigues","suffix":""},{"id":385291493,"identity":"6869f51a-bf3b-401e-adff-74d7ec86857a","order_by":2,"name":"Paulo Vitor de Campos Souza","email":"","orcid":"","institution":"Fondazione Bruno Kessler","correspondingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"Vitor de Campos","lastName":"Souza","suffix":""}],"badges":[],"createdAt":"2024-12-02 17:02:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5566310/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5566310/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70630699,"identity":"f301ab87-b2aa-4a86-8012-867615274b80","added_by":"auto","created_at":"2024-12-05 05:37:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1522976,"visible":true,"origin":"","legend":"","description":"","filename":"2024PaperEVOSeFCMG.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5566310/v1_covered_da8b7ffc-cdf9-4dff-81e7-49e9e331fa78.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eeFCMG - An Evolving Fuzzy Classifier with Participatory Learning and Multivariable Gaussian for Data Stream\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Evolving Fuzzy Systems, Classifier, Data Stream, Participatory Learning, Multivariable Gaussian Membership Function.","lastPublishedDoi":"10.21203/rs.3.rs-5566310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5566310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper introduces a novel evolving fuzzy classifier that begins with no initial structure and develops incrementally through a participatory learning-based clustering algorithm. It employs multivariable Gaussian membership functions for rule antecedents and class outputs for consequents. The classifier's learning algorithm is designed to adjust dynamically by creating, merging, deleting, and updating clusters and rules. Uniquely, it features a 'procrastination' approach where clusters are initially formed in a disabled state to robustly manage outliers and ensure only representative data influence the model. Clusters are refined based on compatibility measures using the Mahalanobis distance, with adjustments to learning rates influenced by the nature of incoming data—slowing for anomalies and accelerating for typical inputs. This mechanism enhances adaptability and model accuracy, distinguishing it from existing fuzzy classifiers. Comparative analyses on binary and multiclass tasks demonstrate its superior or competitive performance, underscoring the classifier's innovative approach to evolving fuzzy classification.\u003c/p\u003e","manuscriptTitle":"eFCMG - An Evolving Fuzzy Classifier with Participatory Learning and Multivariable Gaussian for Data Stream","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-05 05:29:39","doi":"10.21203/rs.3.rs-5566310/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"82ca06c5-ace0-4bd3-b580-636c60cb466a","owner":[],"postedDate":"December 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41043440,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2024-12-05T05:29:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-05 05:29:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5566310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5566310","identity":"rs-5566310","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}