Metalearning for DynamicIntegration in Ensemble Methods

Ensemble methods have been receiving an increasing amount of attention, especially because of their successful application to high visibility problems (e.g., the NetFlix prize). An important challenge in ensemble learning (EL) is the management of the set of models to ensure a high level of accuracy, particularly with large number of models and in highly dynamic environments [49]. One approach to deal with these problems in the context of EL is the dynamic approach, which consists in the selection and combination of the best subset of model(s) for each test instance. An alternative approach to find models that are most suitable for a given set of data is metalearning (MtL). MtL uses data from past experiments to build models that relate the characteristics of learning problems with the behaviour of algorithms [5]. Thus, the general goal of this project is to investigate the use MtL for dynamic integration approaches to EL.