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 …

Ensemble methods that train multiple learners and then combine them to use, with Boosting
and Bagging as representatives, are well-known machine learning approaches. It has …

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 …

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 …

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

Real-world problems often involve the optimization of several objectives under multiple
constraints. An example is the hyper-parameter tuning problem of machine learning …

This work presents PESMOC, Predictive Entropy Search for Multi-objective Bayesian
Optimization with Constraints, an information-based strategy for the simultaneous …