iAdditive: Fast and Fair Feature Importance Estimation for Correlated Features Using Shapley Values

iAdditive: Fast and Fair Feature Importance Estimation for Correlated Features Using Shapley Values | 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 iAdditive: Fast and Fair Feature Importance Estimation for Correlated Features Using Shapley Values Abirami Gunasekaran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8962442/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 Interpreting machine learning models fairly and efficiently remains challenging, particularly when features are correlated. Classical Shapley-based explanations can split attribution among substitutes and are often computationally demanding. This study presents iAdditive, a model-agnostic approach that promotes fairness by grouping highly dependent features and allocating a shared contribution, and improves efficiency via a dynamic coalition heuristic inspired by additive explanation methods. Experiments on simulated datasets with known structure and on NHANES indicate that iAdditive produces faithful global attributions under correlation while achieving substantial runtime reductions compared with KernelSHAP, TreeSHAP, SAGE, and exact Shapley baselines. By balancing fairness, interpretability, and efficiency, iAdditive provides a practical tool for trustworthy decision support in applications such as healthcare. Computational Mathematics Explainable Artificial Intelligence Shapley values additive explanation feature importance model interpretability correlated features 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-8962442","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596622470,"identity":"63f94672-5411-4726-8585-5c55c8960378","order_by":0,"name":"Abirami Gunasekaran","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDADCTBZYZPAwJAAZhoQqeVMGqlaGNsOE9bC38B+Tbqgpk5Osv3sw88FbOfzDI4nMH74wXDYGKfhB3jKpGccO2wszZNuLD2D53axwZkHzJI9DIfNcDroAE+aNA/bgcR5DGkM0jwStxM33EhgkGZgOGyDS4c8WMu/uvp5/M+Yf/MYnANpYf6NT4vBAfZj0rxtzAnSEmls0jwJB0Ba2EC24HSY4WEeZmvevsOGM2c8Y7PmOZCcOPPMwzbLHoN0nN6XO97+8DbPtzp5ifNpzLd5/9kl9h1PPnzjR4W1YQMuPcw8GBHA2EAgItkf4JMdBaNgFIyCUcDAAAC351M0r0Pj0AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Huddersfield","correspondingAuthor":true,"prefix":"","firstName":"Abirami","middleName":"","lastName":"Gunasekaran","suffix":""}],"badges":[],"createdAt":"2026-02-25 02:58:43","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-8962442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8962442/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103474897,"identity":"45907506-7bda-4b99-ab9f-47db1d1a25f5","added_by":"auto","created_at":"2026-02-26 06:41:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1945638,"visible":true,"origin":"","legend":"","description":"","filename":"iAdditiveFastandfairestimation2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8962442/v1_covered_77c55a7d-30c0-46c0-ae64-4a5943edacc8.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eiAdditive: Fast and Fair Feature Importance Estimation for Correlated Features Using Shapley Values\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Huddersfield","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":"Explainable Artificial Intelligence, Shapley values, additive explanation, feature importance, model interpretability, correlated features","lastPublishedDoi":"10.21203/rs.3.rs-8962442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8962442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInterpreting machine learning models fairly and efficiently remains challenging, particularly when features are correlated. Classical Shapley-based explanations can split attribution among substitutes and are often computationally demanding. This study presents iAdditive, a model-agnostic approach that promotes fairness by grouping highly dependent features and allocating a shared contribution, and improves efficiency via a dynamic coalition heuristic inspired by additive explanation methods. Experiments on simulated datasets with known structure and on NHANES indicate that iAdditive produces faithful global attributions under correlation while achieving substantial runtime reductions compared with KernelSHAP, TreeSHAP, SAGE, and exact Shapley baselines. By balancing fairness, interpretability, and efficiency, iAdditive provides a practical tool for trustworthy decision support in applications such as healthcare.\u003c/p\u003e","manuscriptTitle":"iAdditive: Fast and Fair Feature Importance Estimation for Correlated Features Using Shapley Values","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 06:41:32","doi":"10.21203/rs.3.rs-8962442/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":"d1f78541-39ca-473c-88d9-fd732e386147","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63482557,"name":"Computational Mathematics"}],"tags":[],"updatedAt":"2026-02-26T06:41:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 06:41:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8962442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8962442","identity":"rs-8962442","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}