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 …

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 …

This paper presents a machine-learning classifier where computations are performed in a
standard 6T SRAM array, which stores the machine-learning model. Peripheral circuits …

A current focus of intense research in pattern classification is the combination of several
classifier systems, which can be built following either the same or different models and/or …

Abstract The combination of Machine Learning (ML) algorithms is a solution for constructing
stronger predictors than a single one. However, some approximations suggest that …

Abstract Averaged n-Dependence Estimators (A n DE) is an approach to probabilistic
classification learning that learns by extrapolation from marginal to full-multivariate …

This research proposes a Dynamic Ensemble Learning Algorithm based on K-means
(DELAK) for intensive care unit (ICU) mortality prediction. Nowadays, the widely applied …

Ensemble data mining methods, also known as classifier combination, are often used to
improve the performance of classification. Various classifier combination methods such as …

High-dimensional small-size data seriously affects the performance of classifiers. By
combining classifiers, ensemble learning obtains higher accuracy and more robust …

Nearest-neighbor density estimators usually do not work well for high dimensional datasets.
Moreover, they have high time complexity of O (n 2) and require high memory usage …