New Approach Based on the Ensemble Learning Estimator to Maximize Accuracy

Abstract

The objective of this article is to contribute to the development and optimization of ensemble models in machine learning by proposing an innovative method based on homogeneous and heterogeneous families of algorithms. This approach aims to improve the model's generalization and reduce the bias-variance tradeoff, with the ambition of achieving more accurate and robust predictions. To illustrate this approach, we rely on proven techniques such as Random Forest, which is based on the principle of Bagging (Bootstrap Aggregating) to reduce the model's variance, as well as AdaBoost and Gradient Boosting, which belong to the family of sequential ensemble methods that utilize dynamic weighting and gradient descent to progressively correct errors. Additionally, other approaches from supervised and unsupervised learning, such as Support Vector Machines (SVM) for classification, K-means for clustering, and Artificial Neural Networks (ANN), enrich this algorithmic landscape. The central idea of our method is to intelligently combine several ensemble models by applying strategies such as Stacking and Blending to optimize errors at each iteration and enhance the model's generalization capacity. As the saying goes, "unity is strength." This maxim is particularly relevant in the context of building a hybrid machine learning model, as it highlights the importance of collaboration among different classes of algorithms to achieve optimal results. By integrating these various approaches under a unified architecture, we aspire to design a robust ensemble model capable of adapting to the complexity of big data and providing accurate and reliable predictions in a highly variable environment.