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 …

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach,
Ensemble Methods: Foundations and Algorithms shows how these accurate methods are …

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

A decision tree is a predictive model that recursively partitions the covariate's space into
subspaces such that each subspace constitutes a basis for a different prediction function …

Transformer is important to the electric power systems, and its accurate fault diagnosis is still
hard. In the paper, a novel transformer fault diagnosis method using an Internet of Things …

Ensembles, as a widely used and effective technique in the machine learning community,
succeed within a key element—“diversity.” The relationship between diversity and …

The use of machine learning algorithms in finance, medicine, and criminal justice can
deeply impact human lives. As a consequence, research into interpretable machine learning …

Traditional random subspace-based classifier ensemble approaches (RSCE) have several
limitations, such as viewing the same importance for the base classifiers trained in different …

The scenario of classification with imbalanced datasets has gained a notorious significance
in the last years. This is due to the fact that a large number of problems where classes are …