Embedded-Filter ACO using Clustering Based Mutual Information for Feature Selection

Embedded-Filter ACO using Clustering Based Mutual Information for Feature Selection | 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 Embedded-Filter ACO using Clustering Based Mutual Information for Feature Selection S Kumar Reddy Mallidi, Rajeswara Rao Ramisetty This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5871619/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Journal of Combinatorial Optimization → Version 1 posted You are reading this latest preprint version Abstract The performance of any machine learning algorithm depends on the purity of the dataset used. A dataset contains both necessary and unnecessary or redundant features. Feature selection is the process of selecting essential features and removing unnecessary and redundant features. Eliminating unnecessary features also minimizes the computational resources needed for processing and storing the datasets. Feature selection methods are broadly classified as filter-based methods and wrapper-based methods. On the other hand, ant colony optimization (ACO) is one widely used bio-inspired meta-heuristic technique used for the selection of optimal solutions. The widely used heuristic measure in ACO for feature selection techniques is mutual information. However, the traditional mutual information can be used effectively with only categorical features. In this paper, we propose an embedded-filter ACO feature selection technique (EFACO) that is embedded with clustering-based mutual information as a heuristic measure for datasets with continuous features and discrete labels. To show the efficiency of the proposed algorithm, EFACO was used on various datasets, and the selected feature subsets were tested with multiple machine learning algorithms. The results clearly show that the EFACO selects the minimal number of features without diminishing the efficiency of machine learning algorithms in the majority of cases. Theoretical Computer Science Artificial Intelligence and Machine Learning Ant Colony Optimization Bio-Inspired Optimization Techniques Feature Selection Dimensionality Reduction Mutual Information for Continous Features Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Journal of Combinatorial Optimization → 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-5871619","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":404990377,"identity":"b65fbf76-d187-4322-a8db-ca024b771d64","order_by":0,"name":"S Kumar Reddy Mallidi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDADNgbmA0BKQoYItcwwLWwJIC08xGthYOAxAJMENci3nz8m8YPBLo+P/8znVzdqLHgY2A8f3YBPi8GZZDbJHobkYjaJ3G3WOceADuNJS7uBVwtDMhtQ2YHENgnebcY5ILYEjxleLfL9j9kk/4C08J95ZpzzjwgtDDeS2aTBtjDkMD/ObSNCi8GNx8bWMgbJQIelmTHn9knwsBHyi3x/4sObbyrsEuf3H378OedbnRw/++Fj+B3GwMAiwQCOEQY2CTBJQDkIMH9AZ4yCUTAKRsEoQAEAi+U/lqbUPLYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5908-8316","institution":"UCE, JNTUK","correspondingAuthor":true,"prefix":"","firstName":"S","middleName":"Kumar Reddy","lastName":"Mallidi","suffix":""},{"id":404990904,"identity":"07db1cf5-1a86-4c81-9a43-1ae764a7f2e3","order_by":1,"name":"Rajeswara Rao Ramisetty","email":"","orcid":"https://orcid.org/0000-0002-0645-1131","institution":"JNTUGV","correspondingAuthor":false,"prefix":"","firstName":"Rajeswara","middleName":"Rao","lastName":"Ramisetty","suffix":""}],"badges":[],"createdAt":"2025-01-21 08:27:20","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-5871619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5871619/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10878-025-01259-6","type":"published","date":"2025-02-09T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75936942,"identity":"85eebd53-32c5-45da-8b73-d18929a657e5","added_by":"auto","created_at":"2025-02-10 17:15:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":598942,"visible":true,"origin":"","legend":"","description":"","filename":"EFACOCopy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5871619/v1_covered_05074d60-6375-49c1-8784-364878a8e80e.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEmbedded-Filter ACO using Clustering Based Mutual Information for Feature Selection\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Jawaharlal Nehru Technological University, Kakinada","isAcceptedByJournal":true,"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":"Ant Colony Optimization, Bio-Inspired Optimization Techniques, Feature Selection, Dimensionality Reduction, Mutual Information for Continous Features","lastPublishedDoi":"10.21203/rs.3.rs-5871619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5871619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe performance of any machine learning algorithm depends on the purity of the dataset used. A dataset contains both necessary and unnecessary or redundant features. Feature selection is the process of selecting essential features and removing unnecessary and redundant features. Eliminating unnecessary features also minimizes the computational resources needed for processing and storing the datasets. Feature selection methods are broadly classified as filter-based methods and wrapper-based methods. On the other hand, ant colony optimization (ACO) is one widely used bio-inspired meta-heuristic technique used for the selection of optimal solutions. The widely used heuristic measure in ACO for feature selection techniques is mutual information. However, the traditional mutual information can be used effectively with only categorical features. In this paper, we propose an embedded-filter ACO feature selection technique (EFACO) that is embedded with clustering-based mutual information as a heuristic measure for datasets with continuous features and discrete labels. To show the efficiency of the proposed algorithm, EFACO was used on various datasets, and the selected feature subsets were tested with multiple machine learning algorithms. The results clearly show that the EFACO selects the minimal number of features without diminishing the efficiency of machine learning algorithms in the majority of cases.\u003c/p\u003e","manuscriptTitle":"Embedded-Filter ACO using Clustering Based Mutual Information for Feature Selection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 14:40:57","doi":"10.21203/rs.3.rs-5871619/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":"359de5d0-5326-460b-ac57-cc0a00f36ecc","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43255956,"name":"Theoretical Computer Science"},{"id":43255957,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-02-10T16:51:44+00:00","versionOfRecord":{"articleIdentity":"rs-5871619","link":"https://doi.org/10.1007/s10878-025-01259-6","journal":{"identity":"journal-of-combinatorial-optimization","isVorOnly":false,"title":"Journal of Combinatorial Optimization"},"publishedOn":"2025-02-09 00:00:00","publishedOnDateReadable":"February 9th, 2025"},"versionCreatedAt":"2025-01-22 14:40:57","video":"","vorDoi":"10.1007/s10878-025-01259-6","vorDoiUrl":"https://doi.org/10.1007/s10878-025-01259-6","workflowStages":[]},"version":"v1","identity":"rs-5871619","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5871619","identity":"rs-5871619","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}