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.
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]
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Database:	Engineering Source

Description
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]
ISSN:	19322321
DOI:	10.24412/1932-2321-2026-190-420-427