{"paper_id":"21e231ac-c30a-4c6c-9f34-87dca14a6368","body_text":"A hybrid approach for efficient outlier detection using supervised and unsupervised techniques | 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 hybrid approach for efficient outlier detection using supervised and unsupervised techniques C. Jayaramulu, Bondu Venkateswarlu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849853/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 Due to data imbalance and dimensionality, it is difficult to achieve optimal performance when detecting outliers in high-dimensional data. Numerous algorithms were developed in try to solve this issue. However, they have their advantages in identifying outliers from such data and are created using either supervised learning technique or unsupervised learning. While unsupervised learning techniques offer mechanisms for discovering and utilising complicated patterns, supervised learning techniques make use of training data. This paper's key premise is that you may \"combine two methodologies to create a hybrid and reap the benefits of both worlds.\" We put forth a cutting-edge machine learning (ML) framework to evaluate this claim, combining supervised and unsupervised techniques for effective outlier detection. Additionally, we suggested an approach called the Multi-Model Approach for Outlier Detection (MMA-OD). The technique improves performance by utilising the advantages of both supervised and unsupervised learning models. Its strength is getting a better feature space. With several benchmark datasets, the suggested approach is assessed. According to the empirical findings, MMA-OD performs better than many other techniques. High-dimensional data hybrid model for outlier detection machine learning feature space enhancement 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-3849853\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":266393545,\"identity\":\"bc3b0c3e-680e-42ee-a9da-2abff5b38465\",\"order_by\":0,\"name\":\"C. 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