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

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 …

In recent years, classifier ensemble techniques have drawn the attention of many
researchers in the machine learning research community. The ultimate goal of these …

In solving many real-world optimization problems, neither mathematical functions nor
numerical simulations are available for evaluating the quality of candidate solutions. Instead …

Many machine learning tasks involve solving complex optimization problems, such as
working on non-differentiable, non-continuous, and non-unique objective functions; in some …

Selective ensemble learning is a technique that selects a subset of diverse and accurate
basic models in order to generate stronger generalization ability. In this paper, we proposed …

Neural networks are a powerful class of nonlinear functions that can be trained end-to-end
on various applications. While the over-parametrization nature in many neural networks …

Ensemble is a general way of improving the accuracy and stability of learning models,
especially for the generalization ability on small datasets. Compared with tree-based …

We introduce a very general method for high dimensional classification, based on careful
combination of the results of applying an arbitrary base classifier to random projections of …

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