Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach

Authors:
João Mendes-Moreira;Alipio Mario Jorge;Carlos Soares;Jorge Freire Sousa
Affiliations:
Faculdade de Engenharia, Universidade do Porto, DEI, Portugal and LIAAD-INESC Porto L.A., Portugal;LIAAD-INESC Porto L.A., Portugal and Faculdade de Ciências, Universidade do Porto, Portugal;LIAAD-INESC Porto L.A., Portugal and Faculdade de Economia, Universidade do Porto, Portugal;Faculdade de Engenharia, Universidade do Porto, DEIG, Portugal
Venue:
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2009

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Abstract

Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble prediction. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.