Over the last two decades, the machine learning and related communities have conducted
numerous studies to improve the performance of a single classifier by combining several …

An ensemble of models is a set of learning models whose individual predictions are
combined in such a way that component models compensate for each other's weaknesses …

Diabetes mellitus (DM) is a combination of metabolic disorders characterized by elevated
blood glucose levels over a prolonged duration. Undiagnosed DM can give rise to a host of …

This paper proposes a stacking ensemble classification model to classify the different Power
Quality Disturbances (PQDs) in Photovoltaic (PV) integrated power network. For this study …

A Classifier Ensemble combines a finite number of classifiers of same kind or different,
trained simultaneously for a common classification task. The Ensemble efficiently improves …

An ensemble is a collective decision-making system which applies a strategy to combine the
predictions of learned classifiers to generate its prediction of new instances. Early research …

Stacking ensemble learning is one of the most effective integration technologies and is
increasingly applied to near-infrared spectroscopy combined with chemometrics methods …

A variety of methods have been developed in order to tackle a classification problem in the
field of decision support systems. A hybrid prediction scheme which combines several …

Abstract A novel Machine Learning (ML) approach is proposed to assist the dispersion
modelling of road dust in an urban area. The aim is to improve ENFUSER model's coarse …

Random Forest is one of the most popular supervised machine learning algorithms; it is an
ensemble of decision trees combined together to accurately discover more rules and ensure …