{"paper_id":"0426bd97-d8a7-473d-a2a7-321f2b1505b7","body_text":"SVM–XGBoost Geometric–Gradient Architecture (SXGA): A Robust and Interpretable Hybrid Framework for Classification of Psychological Constructs Using DASS-21, SWLS, and BFI-10. | 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 SVM–XGBoost Geometric–Gradient Architecture (SXGA): A Robust and Interpretable Hybrid Framework for Classification of Psychological Constructs Using DASS-21, SWLS, and BFI-10. Yugam padha, Shruti Gupta, Dr. Akshma Chadha, Prof. Vinod Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7966047/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 Understanding psychological constructs such as depression, life satisfaction, and personality traits requires models that balance predictive accuracy, interpretability, and robustness. This study introduces a novel hybrid ensemble framework , termed the SVM–XGBoost Geometric–Gradient Architecture (SXGA) , that fuses margin-based geometric learning with gradient-boosted refinement. Unlike conventional ensembles, SXGA transforms Support Vector Machine (SVM) decision margins into a new feature space before applying XGBoost, achieving a representation-level synthesis that enhances stability and interpretability. Comprehensive evaluation using three psychometric scales— DASS-21 , SWLS , and BFI-10 —demonstrates that the proposed model achieves an average accuracy of 93.6% , an F1-score of 0.91 , and an ROC-AUC of 0.92 , outperforming baseline and stacking models. Rigorous ablation, bootstrap, and fairness analyses confirm that SXGA maintains low variance (<1.5%) and minimal demographic bias (ΔF1 ≤ 0.05). These results indicate that the proposed architecture effectively integrates linear and non-linear decision processes, offering intrinsic interpretability without post-hoc explainers. Overall, SXGA advances hybrid ensemble learning by introducing a geometrically grounded, computationally efficient, and reproducible framework for transparent psychological construct classification. Artificial Intelligence and Machine Learning Full Text Additional Declarations The authors declare no competing interests. This study involved the voluntary participation of adult subjects who provided informed consent prior to completing standardized psychological questionnaires (DASS-21, SWLS, and BFI-10). No personally identifiable information was collected, and all responses were recorded anonymously. In accordance with the institutional guidelines of the University of Jammu and international ethical standards (the Declaration of Helsinki), the study was determined to be exempt from formal ethics committee review as it posed minimal risk to participants. ethics committees of University of Jammu. 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. 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adult subjects who provided informed consent prior to completing standardized psychological questionnaires (DASS-21, SWLS, and BFI-10). No personally identifiable information was collected, and all responses were recorded anonymously. In accordance with the institutional guidelines of the University of Jammu and international ethical standards (the Declaration of Helsinki), the study was determined to be exempt from formal ethics committee review as it posed minimal risk to participants. ethics committees of University of Jammu.\\u003c/p\\u003e\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eSVM–XGBoost Geometric–Gradient Architecture (SXGA): A Robust and Interpretable Hybrid Framework for Classification of Psychological Constructs Using DASS-21, SWLS, and BFI-10.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7966047/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7966047/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eUnderstanding psychological constructs such as depression, life satisfaction, and personality traits requires models that balance predictive accuracy, interpretability, and robustness. This study introduces a \\u003cstrong\\u003enovel hybrid ensemble framework\\u003c/strong\\u003e, termed the \\u003cstrong\\u003eSVM–XGBoost Geometric–Gradient Architecture (SXGA)\\u003c/strong\\u003e, that fuses margin-based geometric learning with gradient-boosted refinement. Unlike conventional ensembles, SXGA transforms Support Vector Machine (SVM) decision margins into a new feature space before applying XGBoost, achieving a representation-level synthesis that enhances stability and interpretability. Comprehensive evaluation using three psychometric scales—\\u003cstrong\\u003eDASS-21\\u003c/strong\\u003e, \\u003cstrong\\u003eSWLS\\u003c/strong\\u003e, and \\u003cstrong\\u003eBFI-10\\u003c/strong\\u003e—demonstrates that the proposed model achieves an average accuracy of \\u003cstrong\\u003e93.6%\\u003c/strong\\u003e, an \\u003cstrong\\u003eF1-score of 0.91\\u003c/strong\\u003e, and an \\u003cstrong\\u003eROC-AUC of 0.92\\u003c/strong\\u003e, outperforming baseline and stacking models. Rigorous ablation, bootstrap, and fairness analyses confirm that SXGA maintains low variance (\\u0026lt;1.5%) and minimal demographic bias (ΔF1 ≤ 0.05). These results indicate that the proposed architecture effectively integrates linear and non-linear decision processes, offering \\u003cstrong\\u003eintrinsic interpretability\\u003c/strong\\u003e without post-hoc explainers. Overall, SXGA advances hybrid ensemble learning by introducing a geometrically grounded, computationally efficient, and reproducible framework for transparent psychological construct classification.\\u003c/p\\u003e\",\"manuscriptTitle\":\"SVM–XGBoost Geometric–Gradient Architecture (SXGA): A Robust and Interpretable Hybrid Framework for Classification of Psychological Constructs Using DASS-21, SWLS, and BFI-10.\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-06 09:49:31\",\"doi\":\"10.21203/rs.3.rs-7966047/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"40e2c3e4-6945-4ef1-8fdb-2fcb5c9be762\",\"owner\":[],\"postedDate\":\"November 6th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":56992196,\"name\":\"Artificial Intelligence and Machine Learning\"}],\"tags\":[],\"updatedAt\":\"2025-11-06T09:49:31+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-06 09:49:31\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7966047\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7966047\",\"identity\":\"rs-7966047\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}