Statistical ensemble method (SEM): a new meta-machine learning approach based on statistical techniques

Authors:
Andrés Yáñez Escolano;Pedro Galindo Riaño;Joaquin Pizarro Junquera;Elisa Guerrero Vázquez
Affiliations:
Departamento de Lenguajes y Sistemas Informáticos, Grupo de ”Sistemas Inteligentes de Computación”, C.A.S.E.M., Universidad de Cádiz, Puerto Real (Cádiz), Spain;Departamento de Lenguajes y Sistemas Informáticos, Grupo de ”Sistemas Inteligentes de Computación”, C.A.S.E.M., Universidad de Cádiz, Puerto Real (Cádiz), Spain;Departamento de Lenguajes y Sistemas Informáticos, Grupo de ”Sistemas Inteligentes de Computación”, C.A.S.E.M., Universidad de Cádiz, Puerto Real (Cádiz), Spain;Departamento de Lenguajes y Sistemas Informáticos, Grupo de ”Sistemas Inteligentes de Computación”, C.A.S.E.M., Universidad de Cádiz, Puerto Real (Cádiz), Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

The goal of combining the outputs of multiple models is to form an improved meta-model with higher generalization capability than the best single model used in isolation. Most popular ensemble methods do specify neither the number of component models nor their complexity. However, these parameters strongly influence the generalization capability of the meta-model. In this paper we propose an ensemble method which generates a meta-model with optimal values for these parameters. The proposed method suggests using resampling techniques to generate multiple estimations of the generalization error and multiple comparison procedures to select the models that will be combined to form the meta-model. Experimental results show the performance of the model on regression and classification tasks using artificial and real databases.