{"paper_id":"3acff0f2-1a86-483f-aec2-9ef7ae8e249f","body_text":"N-Centroid Classifier vs. Machine Learning Models for Data 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 Method Article N-Centroid Classifier vs. Machine Learning Models for Data Classification Hassan I. Abdalla, Aneela Altaf, Loc Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5876238/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 their universal applicability, machine learning models (ML) have been a hot topic during the last two decades. Despite their effectiveness, some ML models exhibit inefficiency, particularly in big data classification. Moreover, some ML models have been seen ineffective on some small datasets. In this regard, due to the growing accessibility of online data, the automatic data classification technique has attracted a lot of study interest. As a result, numerous unique learning strategies have been developed in the text categorization field. The Centroid-Based Classifier (CBC) is one of these most extensively used technique among them. While focusing on enhancing NC classifier, this paper, therefore, aims to briefly investigate the impact of some ML models on small and medium-sized dataset’s classification. Among these models: N-Centroid technique (NC) as a simply-designed classifier, Support Vector Machine (SVM), and Multinomial Bayesian (MNB). Most importantly, this paper introduces an enhanced variation of NC via its integration with two similarity measures, namely, Set Theory Based Similarity Measure (STB-SM) and Improved Cosine Similarity Measure (ISC). The performance of integrated NC classifiers has been seen as promising in terms of both effectiveness and efficiency. Artificial Intelligence and Machine Learning Machine Learning N-Centroid Classifier Support Vector Machine Logistic Regression Multinomial Bayesian Neural Network 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-5876238\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Method Article\",\"associatedPublications\":[],\"authors\":[{\"id\":405308624,\"identity\":\"f791acf9-3ffc-4cf5-b9e8-8aa607250af9\",\"order_by\":0,\"name\":\"Hassan I. 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