Graph-Based model-selection framework for large ensembles

Authors:
Krisztian Buza;Alexandros Nanopoulos;Lars Schmidt-Thieme
Affiliations:
Information Systems and Machine Learning Lab (ISMLL) Samelsonplatz 1, University of Hildesheim, Hildesheim, Germany;Information Systems and Machine Learning Lab (ISMLL) Samelsonplatz 1, University of Hildesheim, Hildesheim, Germany;Information Systems and Machine Learning Lab (ISMLL) Samelsonplatz 1, University of Hildesheim, Hildesheim, Germany
Venue:
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Year:
2010

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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.