Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/99872
Author(s): Joao Mendes Moreira
Alipio Mario Jorge
Carlos Soares
Jorge Freire de Sousa
Title: Ensemble Learning: A Study on Different Variants of the Dynamic Selection Approach
Issue Date: 2009
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 predication. 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.
Subject: Inteligência artificial, Ciências da computação e da informação
Artificial intelligence, Computer and information sciences
Call Number: 61869
URI: http://hdl.handle.net/10216/99872
Source: Machine Learning and Data Mining in Pattern Recognition
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: restrictedAccess
Appears in Collections:FCUP - Artigo em Livro de Atas de Conferência Internacional
FEUP - Artigo em Livro de Atas de Conferência Internacional
FEP - Artigo em Livro de Atas de Conferência Internacional

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