Ensemble Methods for Improved Predictive Modeling in AI
Abstract
Ensemble methods have emerged as powerful tools in the field of artificial intelligence, offering enhanced predictive modeling capabilities through the combination of multiple individual models. This paper explores the fundamental principles and diverse techniques of ensemble learning and their application in various AI domains. We delve into the theoretical underpinnings of ensembles, including bagging, boosting, and stacking, and highlight their advantages in terms of accuracy, robustness, and interpretability. Practical implementation strategies, along with a comparative analysis of ensemble methods, are discussed. Real-world case studies demonstrate how ensembles can effectively improve predictive modeling in tasks such as classification, regression, and anomaly detection. The paper also addresses the challenges and considerations when applying ensemble methods, including computational complexity and model selection. By presenting a comprehensive overview, this work aims to provide AI practitioners and researchers with a deeper understanding of ensemble methods and their potential to enhance AI systems.
Downloads
References
Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.
Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990
Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998
Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654
Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653
Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184
Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672
Dietterich, T. G. (2000). Ensemble methods in machine learning. International Workshop on Multiple Classifier Systems.
Breiman, L. (1996). Bagging predictors. Machine learning, 24(2), 123-140.
Schapire, R. E. (2003). The boosting approach to machine learning: An overview. Nonlinear estimation and classification, 171(1), 149-171.
Wolpert, D. H. (1992). Stacked generalization. Neural networks, 5(2), 241-259.
Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39.
