OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method

OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method | 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 OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method G. Vijayakumar, R. K. Bharathi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4730149/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Nov, 2024 Read the published version in Discover Computing → Version 1 posted 12 You are reading this latest preprint version Abstract Optimizing the performance of Java Virtual Machines (JVMs) [1] is crucial for achieving efficient execution of Java applications. Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE) [2] model to refine feature selection for JVM fine-tuning using machine learning techniques. Traditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections [3] . In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM. To validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance [4] . It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution. JVM RFE Feature Selection GC Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Nov, 2024 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 27 Aug, 2024 Reviews received at journal 22 Aug, 2024 Reviewers agreed at journal 18 Aug, 2024 Reviews received at journal 16 Aug, 2024 Reviewers agreed at journal 16 Aug, 2024 Reviews received at journal 06 Aug, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers invited by journal 30 Jul, 2024 Editor assigned by journal 22 Jul, 2024 Editor invited by journal 22 Jul, 2024 Submission checks completed at journal 19 Jul, 2024 First submitted to journal 12 Jul, 2024 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-4730149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":338637427,"identity":"bb035146-3a4d-423a-8db0-855532f2d77b","order_by":0,"name":"G. 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Feature selection plays a pivotal role in identifying the most relevant parameters for fine-tuning JVMs, thereby enhancing their overall efficiency. This paper presents a novel hybrid approach that integrates both subject matter expertise and Recursive Feature Elimination (RFE)\u003cb\u003e[2]\u003c/b\u003e model to refine feature selection for JVM fine-tuning using machine learning techniques.\u003c/p\u003e \u003cp\u003eTraditional feature selection methods often lack the ability to incorporate domain-specific knowledge, resulting in suboptimal selections\u003cb\u003e[3]\u003c/b\u003e. In contrast, the hybrid approach leverages the expertise of JVM administrators or developers to guide the feature selection process. By integrating domain knowledge into the feature selection pipeline, ensure the inclusion of crucial JVM parameters that may not be captured by automated techniques alone. Furthermore, employed the RFE model, a powerful recursive feature elimination algorithm, to iteratively identify and eliminate irrelevant features from the initial feature set. This iterative process enhances the efficiency of feature selection by systematically pruning less influential parameters, thereby improving the overall performance of the JVM.\u003c/p\u003e \u003cp\u003eTo validate the effectiveness of the hybrid approach, conducted experiments using real-world JVM datasets and compare the performance of the method against existing feature selection techniques. The results demonstrate that the approach not only achieves superior performance in terms of JVM fine-tuning but also provides insights into the significance of domain expertise in optimizing JVM performance\u003cb\u003e[4]\u003c/b\u003e. It contributes to the field of JVM optimization by proposing a novel hybrid approach that combines subject matter expertise with machine learning-based feature selection techniques. By leveraging both domain knowledge and automated algorithms, the approach offers a comprehensive solution for enhancing feature selection in JVM fine-tuning, ultimately leading to improved performance and efficiency in Java application execution.\u003c/p\u003e","manuscriptTitle":"OptiFeat: Enhancing Feature Selection, A Hybrid Approach Combining Subject Matter Expertise and Recursive Feature Elimination Method","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-13 18:47:07","doi":"10.21203/rs.3.rs-4730149/v1","editorialEvents":[{"type":"communityComments","content":1},{"type":"decision","content":"Revision requested","date":"2024-08-27T05:28:19+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-22T10:50:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60811202429491252035747462199025725372","date":"2024-08-18T13:06:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-16T11:36:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186932505400334168526756869591216395626","date":"2024-08-16T11:31:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-06T15:17:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"19079944418309938074083508691654211046","date":"2024-07-30T19:42:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-30T05:19:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-22T10:53:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-22T10:41:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-19T12:09:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2024-07-12T12:06:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1f3d0ce-a304-4e4c-8371-77a1dfc0e407","owner":[],"postedDate":"August 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T19:16:59+00:00","versionOfRecord":{"articleIdentity":"rs-4730149","link":"https://doi.org/10.1007/s10791-024-09483-0","journal":{"identity":"discover-computing","isVorOnly":false,"title":"Discover Computing"},"publishedOn":"2024-11-12 15:57:48","publishedOnDateReadable":"November 12th, 2024"},"versionCreatedAt":"2024-08-13 18:47:07","video":"","vorDoi":"10.1007/s10791-024-09483-0","vorDoiUrl":"https://doi.org/10.1007/s10791-024-09483-0","workflowStages":[]},"version":"v1","identity":"rs-4730149","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4730149","identity":"rs-4730149","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}