{"paper_id":"27f320d1-c765-4859-b1bc-e542fbd7ce9d","body_text":"A New Optimization-Based Framework for Enhanced Feature Selection with the Narwal Optimizer | 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 New Optimization-Based Framework for Enhanced Feature Selection with the Narwal Optimizer Seyyid Ahmed Medjahed, Fatima Boukhatem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5304943/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract The selection of relevant features is a critical step in many machine learning and data analysis tasks, as it can significantly impact the performance and inter-pretability of the resulting models. In this work, we introduce a novel feature selection approach that draws inspiration from the unique movement patterns of the narwhal, a fascinating marine mammal, and the power of binary optimization. The proposed method, named BNO-FS is based on Narwhals Optimizer (NO) which is a new meta-heuristic recently developed recently and never been tested on feature selection problem. A binary version is proposed in this study. A new fitness function is proposed composed of two important terms: the classification accuracy rate obtained by three classifiers and the number of selected features. The algorithm aims to identify the optimal subset of features that maximizes the predictive performance of the model while minimizing the number of selected features. The effectiveness of the Binary Narwhals Optimizer approach is demonstrated through a series of experiments on benchmark datasets, where it is compared to other state-of-the-art feature selection techniques. Feature selection Narwhals Optimizer Classi cation Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviews received at journal 03 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 29 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviews received at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers invited by journal 26 Jun, 2025 Editor assigned by journal 22 Oct, 2024 Submission checks completed at journal 22 Oct, 2024 First submitted to journal 21 Oct, 2024 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-5304943\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":369296876,\"identity\":\"5d7f36ea-c4ae-4e7d-b00d-02b49547fe3e\",\"order_by\":0,\"name\":\"Seyyid Ahmed Medjahed\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"University of Relizane\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Seyyid\",\"middleName\":\"Ahmed\",\"lastName\":\"Medjahed\",\"suffix\":\"\"},{\"id\":369296879,\"identity\":\"f77417da-838a-4e03-bcca-c30096b0b84f\",\"order_by\":1,\"name\":\"Fatima Boukhatem\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Djilali Liabes\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fatima\",\"middleName\":\"\",\"lastName\":\"Boukhatem\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-21 13:38:49\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5304943/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5304943/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":68240640,\"identity\":\"f9aa1f17-aa3f-4134-9612-1db8b1eb8f66\",\"added_by\":\"auto\",\"created_at\":\"2024-11-05 08:08:38\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":797127,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"snarticle.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5304943/v1_covered_c2e2effa-74f5-4505-8126-bcb89443b04a.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A New Optimization-Based Framework for Enhanced Feature Selection with the Narwal Optimizer\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"the-journal-of-supercomputing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)\",\"snPcode\":\"11227\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11227/3\",\"title\":\"The Journal of Supercomputing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Feature selection, Narwhals Optimizer, Classi cation Optimization\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5304943/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5304943/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"The selection of relevant features is a critical step in many machine learning and data analysis tasks, as it can significantly impact the performance and inter-pretability of the resulting models. 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