Enhanced AdaBoost with Adaptive Weighting for Higgs Signal Classification

Enhanced AdaBoost with Adaptive Weighting for Higgs Signal Classification | 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 Enhanced AdaBoost with Adaptive Weighting for Higgs Signal Classification Monchi Estevez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6361754/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 The 2012 Higgs boson discovery at CERN highlighted the difficulty of isolating signals within petabytes of Large Hadron Collider data. This study introduces an innovative AdaBoost extension, building on Freund and Schapire’s framework, with a novel adaptive weighting scheme a' m that tackles class imbalance and high-dimensionality in classifying Higgs boson collisions from the UCI HIGGS dataset. Achieving 70.40% test accuracy, the model leverages an exponential loss function to prioritize challenging data points, optimizing signal detection while balancing overfitting. Results peak at depth 3 and 98 iterations, showcasing enhanced performance over standard methods. This breakthrough demonstrates AdaBoost’s potential for large-scale physics classification, with future refinements like dynamic classifier families proposed to elevate accuracy further. Artificial Intelligence and Machine Learning adaboost class imbalance adaptive weighting 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-6361754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437468724,"identity":"190fb7ce-28ad-4551-91a5-521de5ad0ace","order_by":0,"name":"Monchi Estevez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBACxgYYS/7wASApIUOcFpBaBgm2BBDJQ5xVEC08BiCKsBbmaYcPf/5QcVhOfnbP51c3aix4GNgPH92A12Gz09IkDpw5bMw45+w265xjQIfxpKXdwK8lx4zhYFtaYjND7jbjHDagFgkeMwJa8j9/OPgvrb6NIeeZcc4/orTkMEgcbLBJ4JHIYX6c20aUljQziTPHbAxn8BwzY87tk+BhI+QXw9nJjz9U1EjIy7c3P/6c861Ojp/98DH8WhoQbDYJMIlPOQjII7GZPxBSPQpGwSgYBSMTAADEPkhoClgJBwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0008-7411-2589","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Monchi","middleName":"","lastName":"Estevez","suffix":""}],"badges":[],"createdAt":"2025-04-02 13:36:08","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-6361754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6361754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106093799,"identity":"be0465dc-1ef4-4e23-b340-cf57d315affd","added_by":"auto","created_at":"2026-04-03 11:39:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":464937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6361754/v1_covered_a0372911-02ac-40ef-83bb-9330bd6f406d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEnhanced AdaBoost with Adaptive Weighting for Higgs Signal Classification\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":"adaboost, class imbalance, adaptive weighting","lastPublishedDoi":"10.21203/rs.3.rs-6361754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6361754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe 2012 Higgs boson discovery at CERN highlighted the difficulty of isolating signals within petabytes of Large Hadron Collider data. 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