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The challenge of achieving high predictive accuracy in machine learning (ML) lies in managing model bias, variance, and generalization. Ensemble learning techniques, including bagging, boosting, and stacking, have proven effective in mitigating individual model limitations by combining the outputs of multiple models. This article explores how a meta-learning framework can be used to enhance ensemble learning. Specifically, it outlines the theoretical foundation and implementation steps of dynamic ensemble selection and meta-model stacking to achieve robust predictive systems. By focusing on method design rather than specific experimental results, this article serves as a guide for researchers and practitioners aiming to develop adaptive, accurate, and interpretable ensemble learning systems.
Keywords:
Ensemble Learning · Meta-Learning · Predictive Accuracy · Stacking · AI · Model Selection
Cite Article:
"Enhancing Predictive Accuracy with Ensemble Learning: A Meta-Learning Approach Using AI and ML Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.b43-b44, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506107.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator