Abstract. Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniqu...
Frank Rosenthal, Peter Benjamin Volk, Martin Hahma...
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, ...
This paper is motivated to improve the performance of individual ensembles using a hybrid mechanism in the regression setting. Based on an error-ambiguity decomposition, we formal...
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...
Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regre...