A feature classification learning method based on multi-objective swarm intelligence optimization

A feature classification learning method based on multi-objective swarm intelligence optimization | 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 A feature classification learning method based on multi-objective swarm intelligence optimization Fumin Ma, Yuqi Zhong, Yihui Chen, Xiaojian Ding, Fan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4991509/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 In the context of the exponential growth in data volume, the efficient and accurate classification of large and often redundant datasets has become a critical aspect of data processing and analysis. The Extreme Learning Machine (ELM), known for its randomized parameter selection and efficient hidden layer mapping, is particularly adept at classifying nonlinear problems, especially when dealing with large-scale data. However, the random initialization of hidden layer parameters in a single ELM network introduces inherent uncertainty, which can lead to significant fluctuations in data representation and processing within the network. These fluctuations may result in instability in classification outcomes. Additionally, when the training data contain noise, individual ELMs may be prone to overfitting.To address these challenges, this paper presents a novel enhanced ELM classification algorithm, referred to as ELM-MOFGA. The algorithm utilizes a Multi-Objective Fitness Genetic Algorithm (MOFGA) to evaluate and optimize a series of ELM classifiers, incorporating a more robust double error rate evaluation to improve the stability and reliability of the classifiers. The most optimal predictions from the ensemble of multiple ELM models are then aggregated through a voting mechanism, significantly enhancing the models' overall performance and stability. Experimental results demonstrate that the proposed ELM-MOFGA algorithm achieves superior prediction accuracy when applied to the Cancer, Diabetic, and Fourclass datasets. extreme learning machine (ELM) classifier voting mechanism classifier optimization genetic algorithm Full Text Additional Declarations No competing interests reported. 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. 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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-4991509","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":351265391,"identity":"7473cb08-a93d-4af8-a575-ff52d076b04f","order_by":0,"name":"Fumin Ma","email":"","orcid":"","institution":"College of Information Engineering, Nanjing University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Fumin","middleName":"","lastName":"Ma","suffix":""},{"id":351265395,"identity":"5a5a4c25-5435-4fab-8be4-1169acc0fda4","order_by":1,"name":"Yuqi Zhong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3QvWrDMBDA8QtXrouoVwWC8woHBq99FR0BTyl0Kh48yCTUQ9O9j9GpZEwwOItK14z2I3RroUOcOSFytg76TTfcH30ABME/RLdl2cpfEd/XuG1NXviTO1XX3NkmgR3NuHWNP4l1lo07i2K/VDruljjgYsqxljUlo4VKc7EEUfViPG9ZMYubxIj0tJf1BLT7fPeeYgxRQogfe3EErB88iZ7zxhDKCiF9lGcckmRi+015w5sUhiX9J4NxTcJIM90PyvuWaVVWvz95EXNUb7+PQ1S9Xk5OqOvWgyAIgrMOJvJJNsoq3FQAAAAASUVORK5CYII=","orcid":"","institution":"College of Information Engineering, Nanjing University of Finance and Economics","correspondingAuthor":true,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Zhong","suffix":""},{"id":351265396,"identity":"27f7c958-4581-4c7f-9ab7-6075b83c0f51","order_by":2,"name":"Yihui Chen","email":"","orcid":"","institution":"College of Information Engineering, Nanjing University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Yihui","middleName":"","lastName":"Chen","suffix":""},{"id":351265397,"identity":"07afe1c9-2f30-414f-b942-b0da4e05efce","order_by":3,"name":"Xiaojian Ding","email":"","orcid":"","institution":"College of Information Engineering, Nanjing University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Xiaojian","middleName":"","lastName":"Ding","suffix":""},{"id":351265400,"identity":"105abc52-dfa1-4af9-a5c4-128514158c8c","order_by":4,"name":"Fan Yang","email":"","orcid":"","institution":"College of Information Engineering, Nanjing University of Finance and Economics","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-08-28 13:39:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4991509/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4991509/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70720361,"identity":"cdbf19bd-3e75-48c3-b6b9-7e8cfc44d696","added_by":"auto","created_at":"2024-12-06 02:54:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340701,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitleVersion592.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4991509/v1_covered_a2ca43c7-4848-4883-9f7c-3fba18d9d414.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A feature classification learning method based on multi-objective swarm intelligence optimization","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"extreme learning machine (ELM), classifier, voting mechanism, classifier optimization, genetic algorithm","lastPublishedDoi":"10.21203/rs.3.rs-4991509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4991509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the context of the exponential growth in data volume, the efficient and accurate classification of large and often redundant datasets has become a critical aspect of data processing and analysis. The Extreme Learning Machine (ELM), known for its randomized parameter selection and efficient hidden layer mapping, is particularly adept at classifying nonlinear problems, especially when dealing with large-scale data. However, the random initialization of hidden layer parameters in a single ELM network introduces inherent uncertainty, which can lead to significant fluctuations in data representation and processing within the network. These fluctuations may result in instability in classification outcomes. Additionally, when the training data contain noise, individual ELMs may be prone to overfitting.To address these challenges, this paper presents a novel enhanced ELM classification algorithm, referred to as ELM-MOFGA. The algorithm utilizes a Multi-Objective Fitness Genetic Algorithm (MOFGA) to evaluate and optimize a series of ELM classifiers, incorporating a more robust double error rate evaluation to improve the stability and reliability of the classifiers. 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