ADAPTIVE NONLINEAR ENSEMBLE LEARNING WITH DISTRIBUTION-FREE GENERALIZATION BOUNDS FOR HIGH-DIMENSIONAL DATA.
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| Title: | ADAPTIVE NONLINEAR ENSEMBLE LEARNING WITH DISTRIBUTION-FREE GENERALIZATION BOUNDS FOR HIGH-DIMENSIONAL DATA. |
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| Authors: | RATHER, KHALID UL ISLAM1 khalidstat34@gmail.com, NANDAN, DURGESH1 durgeshnandano51@gmail.com |
| Source: | Reliability: Theory & Applications. Mar2026, Vol. 21 Issue 1, p420-427. 8p. |
| Subjects: | Ensemble learning, Statistical learning, Robust statistics, Statistical reliability, Prediction models |
| Abstract: | Ensemble learning has become a cornerstone of modern machine learning, yet most existing approaches such as bagging, boosting, and random forests lack theoretical guarantees in high-dimensional, nonlinear data regimes. In this study, we propose an Adaptive Nonlinear Ensemble Learning (ANEL) framework that integrates data-dependent model selection with distribution-free statistical guarantees. The framework adaptively weights nonlinear base learners according to their local predictive relevance, thereby improving both accuracy and robustness in heterogeneous, high-dimensional datasets. Unlike conventional methods that rely on asymptotic assumptions, our approach provides distribution-free generalization error bounds using concentration inequalities and PAC-Bayesian analysis. Through theoretical derivations and empirical validation on synthetic and real-world datasets (including biomedical imaging and climate prediction), ANEL demonstrates: (i) provable reduction in generalization error compared to standard ensemble methods, (ii) scalability to datasets with millions of features through parallel optimization, and (iii) robustness against covariate shift and heavy-tailed distributions. This contribution advances ensemble methodology by bridging algorithmic adaptivity with rigorous statistical guarantees, offering a novel paradigm for reliable machine learning in high-dimensional environments. [ABSTRACT FROM AUTHOR] |
| Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192779649 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ADAPTIVE NONLINEAR ENSEMBLE LEARNING WITH DISTRIBUTION-FREE GENERALIZATION BOUNDS FOR HIGH-DIMENSIONAL DATA. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22RATHER%2C+KHALID+UL+ISLAM%22">RATHER, KHALID UL ISLAM</searchLink><relatesTo>1</relatesTo><i> khalidstat34@gmail.com</i><br /><searchLink fieldCode="AR" term="%22NANDAN%2C+DURGESH%22">NANDAN, DURGESH</searchLink><relatesTo>1</relatesTo><i> durgeshnandano51@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Reliability%3A+Theory+%26+Applications%22">Reliability: Theory & Applications</searchLink>. Mar2026, Vol. 21 Issue 1, p420-427. 8p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+learning%22">Statistical learning</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+reliability%22">Statistical reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Ensemble learning has become a cornerstone of modern machine learning, yet most existing approaches such as bagging, boosting, and random forests lack theoretical guarantees in high-dimensional, nonlinear data regimes. In this study, we propose an Adaptive Nonlinear Ensemble Learning (ANEL) framework that integrates data-dependent model selection with distribution-free statistical guarantees. The framework adaptively weights nonlinear base learners according to their local predictive relevance, thereby improving both accuracy and robustness in heterogeneous, high-dimensional datasets. Unlike conventional methods that rely on asymptotic assumptions, our approach provides distribution-free generalization error bounds using concentration inequalities and PAC-Bayesian analysis. Through theoretical derivations and empirical validation on synthetic and real-world datasets (including biomedical imaging and climate prediction), ANEL demonstrates: (i) provable reduction in generalization error compared to standard ensemble methods, (ii) scalability to datasets with millions of features through parallel optimization, and (iii) robustness against covariate shift and heavy-tailed distributions. This contribution advances ensemble methodology by bridging algorithmic adaptivity with rigorous statistical guarantees, offering a novel paradigm for reliable machine learning in high-dimensional environments. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Reliability: Theory & Applications is the property of International Group on Reliability and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.24412/1932-2321-2026-190-420-427 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 8 StartPage: 420 Subjects: – SubjectFull: Ensemble learning Type: general – SubjectFull: Statistical learning Type: general – SubjectFull: Robust statistics Type: general – SubjectFull: Statistical reliability Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: ADAPTIVE NONLINEAR ENSEMBLE LEARNING WITH DISTRIBUTION-FREE GENERALIZATION BOUNDS FOR HIGH-DIMENSIONAL DATA. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: RATHER, KHALID UL ISLAM – PersonEntity: Name: NameFull: NANDAN, DURGESH IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19322321 Numbering: – Type: volume Value: 21 – Type: issue Value: 1 Titles: – TitleFull: Reliability: Theory & Applications Type: main |
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