• Minakshi Chandel
• Heena Arora

Ensemble learning techniques have emerged as powerful tools in the field of data mining, leveraging the strength of multiple models to enhance predictive performance and robustness. This paper provides a comprehensive review of ensemble learning methods applied in data mining applications. The discussion encompasses various ensemble strategies, including bagging, boosting, and stacking, elucidating their theoretical foundations and practical implementations. Furthermore, the paper explores the advantages and challenges associated with ensemble learning, examining its effectiveness in improving predictive accuracy, handling diverse datasets, and mitigating overfitting. Real-world case studies and applications of ensemble learning in different domains are discussed to showcase its versatility and efficacy. Additionally, the paper sheds light on current research trends, open challenges, and potential future directions in the evolving landscape of ensemble learning in data mining. This review aims to offer researchers, practitioners, and enthusiasts a comprehensive understanding of the state-of-the-art in ensemble learning techniques and their applications in the context of data mining.

Ensemble learning techniques, Data Mining, Boosting, Bagging, Stacking

"A Review on Ensemble Learning Algorithms in Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.c713-c719, June-2024, Available :http://www.jetir.org/papers/JETIR2406287.pdf

"A Review on Ensemble Learning Algorithms in Data Mining", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppc713-c719, June-2024, Available at : http://www.jetir.org/papers/JETIR2406287.pdf