Ensemble of Neural Networks Augmented with Noise Elimination

Ensemble of Neural Networks Augmented with Noise Elimination | 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 Ensemble of Neural Networks Augmented with Noise Elimination Chapala Maharan, Ch Sanjeev Kumar Dash, Ajit Kumar Behera, Satchidananda Dehuri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6475020/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 Developing a classifier (single) for determiningclass labels for unseen patterns in the life science domain is very common in the field of data mining and machine learning. However, as such data are very sensitive to noise/outliers, a classifier (single) in this context may not always be treated as a robust classification method. The literature has instead advocated combining many classifiers to increase overall accuracy, reduce the risk of classifier selection, and increase the robustness of the classifier. Therefore, in this work, we developed an ensemble of classifiers augmented with noise identification and a novel elimination method.This work is broadly twofold; for fold one, we use the density-based spatial clustering of applications with noise (DBSCAN) clustering technique to identify noise/outliers, which are subsequently eliminated by a novel method based on the high-sensitivity zone (HSZ) and keeping eye on the imbalance of class distribution. In the second step, the model is built using four base classifiers, such as multilayerperceptrons (MLPs) with back-propagation learning, radial basis function networks (RBFNs), extreme learning machines (ELMs), and functional link artificial neural networks (FLANNs). We conducted experimental studies on eight life science datasets collected from the UCI repository. The experimental study results support the claim that the suggested model has the potential to be more beneficial than classifiers (single)/ nonensemble classifiers. Artificial neural network Radial basis function neural networks Extreme learning machine Functional link neural network Density-based spatial clustering of applications with noise 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. 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-6475020","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448929914,"identity":"f99f5ae7-8073-4894-b0dd-69034872a85e","order_by":0,"name":"Chapala Maharan","email":"","orcid":"","institution":"Parala Maharaja Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Chapala","middleName":"","lastName":"Maharan","suffix":""},{"id":448929915,"identity":"253d4d58-fce8-4399-82c4-351fbf0af4e0","order_by":1,"name":"Ch Sanjeev Kumar Dash","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYJACAzB5gIGN4QMDQwJpWhhnEKuFAaaFmYcYLfLtZw8UfKg4zMB3/Pizx7Ztdnn87A2MHz7m4HHUmbwEwxlnDjNInskxN85tSy6W7DnALDlzGz5/5BgY87alMRgcyGGTzm1jTtxwI4GNmRePFvn+NwbGf/8BtZx//kzasq2esBaGG0BbGBtsGAxuJJhJM7YdJqzF4MYbA8OeYzYMkjfemEn2nDueOLPnYDNev8j355gZ/KiRYOA7n/5M4kdZdWI/e/PBDx/xOYyBgQ0UlfUNICYjG5hswKseCJgfINh/CCkeBaNgFIyCkQgA88ZUAhWqN5QAAAAASUVORK5CYII=","orcid":"","institution":"Silicon Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ch","middleName":"Sanjeev Kumar","lastName":"Dash","suffix":""},{"id":448929916,"identity":"58a26332-a262-497e-8436-fb918416e9ea","order_by":2,"name":"Ajit Kumar Behera","email":"","orcid":"","institution":"Silicon Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ajit","middleName":"Kumar","lastName":"Behera","suffix":""},{"id":448929917,"identity":"0043c97c-8f5c-4acb-8ab7-5ec737b81098","order_by":3,"name":"Satchidananda Dehuri","email":"","orcid":"","institution":"Fakir Mohan University","correspondingAuthor":false,"prefix":"","firstName":"Satchidananda","middleName":"","lastName":"Dehuri","suffix":""}],"badges":[],"createdAt":"2025-04-18 01:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6475020/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6475020/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88682434,"identity":"928a14fb-65b8-4fd4-b28c-fbb46ecdf0cc","added_by":"auto","created_at":"2025-08-09 10:16:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":772054,"visible":true,"origin":"","legend":"","description":"","filename":"Ensemblerevised18.04.25.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6475020/v1_covered_7fee22b2-8a18-479b-a9cd-8c8a446a7e9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Ensemble of Neural Networks Augmented with Noise Elimination","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":"Artificial neural network, Radial basis function neural networks, Extreme learning machine, Functional link neural network, Density-based spatial clustering of applications with noise","lastPublishedDoi":"10.21203/rs.3.rs-6475020/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6475020/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDeveloping a classifier (single) for determiningclass labels for unseen patterns in the life science domain is very common in the field of data mining and machine learning. 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