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

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 …

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

The accumulation of large amounts of historical data has led to the wide application of
ensemble learning over the past few decades, but the balance between the individual …

It is well known that the decision-making on treating gastric cancer is usually the summary of
several experts' advice. Moreover, the interpretability and reliability of a model used to assist …

The diversity and the individual accuracies in an ensemble system are usually two opposite
objects, which is ignored in most preliminary ensemble learning algorithms. To alleviate this …

Ensemble learning is an algorithm that utilizes various types of classification models. This
algorithm can enhance the prediction efficiency of component models. However, the …

Imbalance dataset is one of the challenge in machine learning to predict the correct class
and one state of art solution is Ensemble method. Ensemble method predicts the correct …

Traditional ensemble learning approaches explore the feature space and the sample space,
respectively, which will prevent them to construct more powerful learning models for noisy …

In this paper, we propose a weighted multiple classifier framework based on random
projections. Similar to the mechanism of other homogeneous ensemble methods, the base …