In machine learning, two approaches outperform traditional algorithms: ensemble learning
and deep learning. The former refers to methods that integrate multiple base models in the …

Due to the dominant position of deep learning (mostly deep neural networks) in various
artificial intelligence applications, recently, ensemble learning based on deep neural …

Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …

Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …

Ensemble learning techniques have achieved state-of-the-art performance in diverse
machine learning applications by combining the predictions from two or more base models …

During the last decades, in the area of machine learning and data mining, the development
of ensemble methods has gained a significant attention from the scientific community …

Ensemble learning is fast becoming a popular choice for machine learning models in the
data science world. Ensemble methods combine the output of machine learning models in …

Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

In this paper, we present an Adaptive Ensemble Learning framework that aims to boost the
performance of deep neural networks by intelligently fusing features through ensemble …

Ensemble methods are popular strategies for improving the predictive ability of a machine
learning model. An ensemble consists of a set of individually trained base learners/models …