In matters of great importance that have financial, medical, social, or other implications, we
often seek a second opinion before making a decision, sometimes a third, and sometimes …

The goal of ensemble regression is to combine several models in order to improve the
prediction accuracy in learning problems with a numerical target variable. The process of …

Modern top-performing object detectors depend heavily on backbone networks, whose
advances bring consistent performance gains through exploring more effective network …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

This comprehensive encyclopedia, with over 250 entries in an AZ format, provides easy
access to relevant information for those seeking entry into any aspect within the broad field …

Over the last couple of decades, multiple classifier systems, also called ensemble systems
have enjoyed growing attention within the computational intelligence and machine learning …

Coronary heart disease (CHD) is one of the severe health issues and is one of the most
common types of heart diseases. It is the most frequent cause of mortality across the globe …

While performing favourably on the independent and identically distributed (iid) instances,
most of the existing neural methods for vehicle routing problems (VRPs) struggle to …

Ensemble approaches to classification and regression have attracted a great deal of interest
in recent years. These methods can be shown both theoretically and empirically to …

Ensembles are a widely used and effective technique in machine learning—their success is
commonly attributed to the degree of disagreement, or 'diversity', within the ensemble. For …