DMCA
Graph-based Model-Selection Framework for Large Ensembles
BibTeX
@MISC{Buza_graph-basedmodel-selection,
author = {Krisztian Buza and Ros Nanopoulos and Lars Schmidt-thieme},
title = {Graph-based Model-Selection Framework for Large Ensembles},
year = {}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Abstract. The intuition behind ensembles is that different prediciton models compensate each other’s errors if one combines them in an appropriate way. In case of large ensembles a lot of different prediction models are available. However, many of them may share similar error characteristics, which highly depress the compensation effect. Thus the selection of an appropriate subset of models is crucial. In this paper, we address this problem. As major contribution, for the case if a large number of models is present, we propose a graph-based framework for model selection while paying special attention to the interaction effect of models. In this framework, we introduce four ensemble techniques and compare them to the state-of-the-art in experiments on publicly available real-world data.
Keyphrases
large ensemble graph-based model-selection framework compensation effect appropriate way special attention different prediciton model share similar error characteristic interaction effect different prediction model large number major contribution ensemble technique available real-world data graph-based framework model selection appropriate subset
