Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data

Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data | 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 Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data Mahmoud Alimoradi, Arman Daliri, Mahdieh Zabihimayvan, Reza Sadeghi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4009264/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 Proper grouping in classifier algorithms is a critical element of supervised learning. The first step in this is to have the correct data. Data that has a problem is worse than not having it. One of the biggest problems inherent in natural data is its imbalance. For a classifier algorithm to achieve its best performance, the first step is to fix the problem of data imbalance. To work with real datasets, the first step is to balance the data. The main problem with existing algorithms is to duplicate minority data and generate data that make outlines part of the primary data. The Statistic Deviation Mode Balancer (SDMB) algorithm solves this problem by making samples that adhere to the original data structure. Our proposed algorithm generates data that is very similar to the original data with the help of standard deviation and the amount of minor data mode and moving away from the majority part. Using these two parameters, the SDMB algorithm avoids Outlier data and generates clean data. The output of this algorithm is a balance datasheet that helps classifier algorithms learn the best way from the data. Different classifier algorithms with entirely different methods have been tested to prove this point. First, we balanced the different datasets with our method. Then, with varying classifier algorithms, we compared it with other existing algorithms. This experiment showed that our proposed algorithm is superior to other competitors and can be used in the work process of real datasets. Imbalanced data classification data sampling classification algorithm classification diagnosis balancer 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. 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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-4009264","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276122001,"identity":"9bd65d4a-fb5a-484a-9771-93ba953cd5b4","order_by":0,"name":"Mahmoud Alimoradi","email":"data:image/png;base64,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","orcid":"","institution":"Shafagh Institute of Higher Education","correspondingAuthor":true,"prefix":"","firstName":"Mahmoud","middleName":"","lastName":"Alimoradi","suffix":""},{"id":276122002,"identity":"8efd5324-96e2-4fce-868a-9df1242388bf","order_by":1,"name":"Arman Daliri","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Arman","middleName":"","lastName":"Daliri","suffix":""},{"id":276122003,"identity":"96883ef0-345f-485f-9d7c-c74293b77393","order_by":2,"name":"Mahdieh Zabihimayvan","email":"","orcid":"","institution":"Central Connecticut State University","correspondingAuthor":false,"prefix":"","firstName":"Mahdieh","middleName":"","lastName":"Zabihimayvan","suffix":""},{"id":276122004,"identity":"3ba60fbb-8228-4a70-9529-b6d9a9f10b14","order_by":3,"name":"Reza Sadeghi","email":"","orcid":"","institution":"Marist College","correspondingAuthor":false,"prefix":"","firstName":"Reza","middleName":"","lastName":"Sadeghi","suffix":""}],"badges":[],"createdAt":"2024-03-03 16:51:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4009264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4009264/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64329656,"identity":"6c78d7c7-3fd7-43d8-adb9-5d3f0fa25d54","added_by":"auto","created_at":"2024-09-11 18:07:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2497006,"visible":true,"origin":"","legend":"","description":"","filename":"StatisticDeviationModeBalancerSDMB.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4009264/v1_covered_b9c51204-c42e-437e-9c9e-e4a4c8f13f8b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Statistic Deviation Mode Balancer (SDMB): A novel sampling algorithm for imbalanced data","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":"Imbalanced data, classification, data sampling, classification algorithm, classification diagnosis, balancer","lastPublishedDoi":"10.21203/rs.3.rs-4009264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4009264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProper grouping in classifier algorithms is a critical element of supervised learning. 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