A Comparative Evaluation of Outlier Detection in Categorical and Mixed Data

A Comparative Evaluation of Outlier Detection in Categorical and Mixed Data | 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 Comparative Evaluation of Outlier Detection in Categorical and Mixed Data Felippe Pires Ferreira, Robson Leonardo Ferreira Cordeiro This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8875243/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Outlier detection is essential in different domains such as cybersecurity and fraud detection, to name a few. However, identifying the best way to detect outliers is often a challenge. Although most algorithms are designed for numerical data, many real-world datasets contain categorical attributes or a mixture of categorical and numerical ones. Given a dataset with one or more categorical attributes, how to detect the outliers? This survey evaluates three potential solutions: (1) applying algorithms that can process categorical data directly, (2) converting categorical attributes into numerical ones before the detection, and (3) removing categorical attributes so that only the numerical ones are considered in the detection. We performed experiments using 47 datasets and 14 detection algorithms, and demonstrated that Solution (1) is usually preferred, especially when employing the detection algorithm CBRW. However, Solution (2) with detection algorithms such as iForest and KNN-outlier achieves better results in certain contexts, being influenced by the data characteristics. Based on these findings, we also introduce a predictive model that achieves 80% accuracy in identifying the best strategy to process new datasets among the three solutions studied. Additionally, we compared approaches to convert categorical attributes into numerical ones, and showed that the Correspondence Analysis data-conversion method often yields the best results. This survey provides comparative insights, methodological guidance, and predictive support for outlier detection in categorical and mixed data. Categorical Data Outlier Detection Comparison of Approaches Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 13 Mar, 2026 Reviewers invited by journal 23 Feb, 2026 Editor assigned by journal 18 Feb, 2026 Submission checks completed at journal 15 Feb, 2026 First submitted to journal 13 Feb, 2026 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. 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