This paper studies the greedy ensemble selection family of algorithms for ensembles of regression models. These algorithms search for the globally best subset of regresmaking loca...
Ioannis Partalas, Grigorios Tsoumakas, Evaggelos V...
: Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organ...
In this work we present a novel approach to ensemble learning for regression models, by combining the ensemble generation technique of random subspace method with the ensemble int...
Niall Rooney, David W. Patterson, Sarab S. Anand, ...
Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization abilit...
Abstract. This article presents a comprehensive study of different ensemble pruning techniques applied to a bagging ensemble composed of decision stumps. Six different ensemble p...