Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society

Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society | 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 Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society S.R Divyasri, R Saranya, P.Kathiravan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2550707/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Crimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans. Artificial Intelligence and Machine Learning Crime prediction Dynamic ensemble algorithms Machine Learning Supervised Learning KNORA Full Text Additional Declarations Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-2550707","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":174209866,"identity":"abbce5ee-a91f-4df9-bd9c-ee22b44b7537","order_by":0,"name":"S.R Divyasri","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"S.R","middleName":"","lastName":"Divyasri","suffix":""},{"id":174209867,"identity":"5e5a0ba1-7bba-4f4a-b4ba-c85f30aa26f5","order_by":1,"name":"R Saranya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBADOTiLjVgtxgwMzCRqSWyAaSEI5NvPPvxc2GaX3j8j/9gDhho7Bj7pBvxaDM6kG0vPbEvOnXEjmd2A4VgyA5vMAQJaGNIYpHnbmHM3SCSzSTCwHWBgk0gg4LD+Z8y/edvq0w3AWv4RoYXhRhob0JbDCWAtjG1EaDG48YzNmufcccMZZx6bSST2JfMQ4bA05ts8ZdXy/O2JzyQ+fLOTk59ByGEgwAiLPqBiHiLUg8AfItWNglEwCkbByAQAIb41retVSJoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2308-772X","institution":"Central University of Tamil Nadu","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"R","middleName":"","lastName":"Saranya","suffix":""},{"id":174209868,"identity":"a0f03f34-ac31-4f80-b519-a4808d5e8a66","order_by":2,"name":"P.Kathiravan","email":"","orcid":"","institution":"Central University of Tamil Nadu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"","middleName":"","lastName":"P.Kathiravan","suffix":""}],"badges":[],"createdAt":"2023-02-04 16:22:16","currentVersionCode":2,"declarations":"","doi":"10.21203/rs.3.rs-2550707/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-2550707/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":32657107,"identity":"a15140f2-43a0-4364-a40f-50b8bc98871f","added_by":"auto","created_at":"2023-02-08 16:08:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1160582,"visible":true,"origin":"","legend":"","description":"","filename":"crimeprediction1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2550707/v2/768cdc5c79f486dfb76dfe02.pdf"}],"financialInterests":"\u003cp\u003eDeclaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e","formattedTitle":"\u003cp\u003e\u003cstrong\u003eComprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Central University of Tamil Nadu","isAcceptedByJournal":false,"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":"Crime prediction, Dynamic ensemble algorithms, Machine Learning, Supervised Learning, KNORA","lastPublishedDoi":"10.21203/rs.3.rs-2550707/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2550707/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCrimes are a social issue that affects not only an individual but also humanity. Crime classification techniques for crime forecasting are an emerging research area. generally, Crime data are centrally organized with regular maintenance of the criminal registers that can aid officers in sharing observations and improve early alert approaches to keep the citizens secure within their towns. Hence, the aim of this study is to compare the performance of the state-of-the-art Dynamic Ensemble Selection of Classifier algorithms for predicting crime. We used five different benchmark crime datasets (Chicago, San Francisco, Pheonix, Boston, and Vancouver) for this experimental research work. The performance of the state-of-the-art dynamic ensemble selection of classifiers algorithms was evaluated and compared using various performance evaluation metrics such as accuracy, F1-score, precision, and recall. The KNORA Dynamic ensemble algorithms, which select the subset of ensemble members before the forecasting, outperformed the typical machine learning algorithms, and also the traditional ensemble algorithm techniques in terms of accuracy showed that the dynamic ensemble algorithms are more powerful. This ability to predict crimes within urban societies can help citizens, and law enforcement makes precise informed conclusions and preserves the neighborhoods more unassailably to improve the quality of life for humans.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis of Classical Machine Learning models and Ensemble methods for predicting Crime in urban society","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2023-02-08 16:08:11","doi":"10.21203/rs.3.rs-2550707/v2","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}},{"code":1,"date":"2023-02-07 17:58:11","doi":"10.21203/rs.3.rs-2550707/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":"24e462a6-0365-4cfe-b35f-c5ea93a541aa","owner":[],"postedDate":"February 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":19061111,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2023-02-07T17:58:11+00:00","versionOfRecord":[],"versionCreatedAt":"2023-02-08 16:08:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-2550707","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2550707","identity":"rs-2550707","version":["v2"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}