Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Making holistic schema matching robust: an ensemble approach
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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Ensembles of classifiers have been studied for some time. It is widely known that weak learners should be accurate and diverse. However, in the real world there are many constraints and few have been said about the robustness of ensembles and how to develop it. In the context of random subspace methods, this paper addresses the question of developing ensembles to face problems under time constraints. Experiments show that selecting weak learners based on their accuracy can be used to create robust ensembles. Thus, the selection pressure in ensembles is a key technique to create not just effective ensembles but also robust ones. Moreover, the experiments motivate further research on ensembles made of low dimensional classifiers which achieve general accurate results.