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Solving regression problems using competitive ensemble models

conference contribution
posted on 2002-01-01, 00:00 authored by Yakov Frayman, Bernard RolfeBernard Rolfe, G Webb
The use of ensemble models in many problem domains has increased significantly in the last fewyears. The ensemble modeling, in particularly boosting, has shown a great promise in improving predictive performance of a model. Combining the ensemble members is normally done in a co-operative fashion where each of the ensemble members performs the same task and their predictions are aggregated to obtain the improved performance. However, it is also possible to combine the ensemble members in a competitive fashion where the best prediction of a relevant ensemble member is selected for a particular input. This option has been previously somewhat overlooked. The aim of this article is to investigate and compare the competitive and co-operative approaches to combining the models in the ensemble. A comparison is made between a competitive ensemble model and that of MARS with bagging, mixture of experts, hierarchical mixture of experts and a neural network ensemble over several public domain regression problems that have a high degree of nonlinearity and noise. The empirical results showa substantial advantage of competitive learning versus the co-operative learning for all the regression problems investigated. The requirements for creating the efficient ensembles and the available guidelines are also discussed.

History

Title of proceedings

AI 2002 : Advances in artificial intelligence : proceedings of the 15th Australian Joint Conference on Artificial Intelligence

Event

Australian Joint Conference on Artificial Intelligence (15th : 2002 : Canberra, A.C.T.)

Pagination

511 - 522

Publisher

Springer

Location

Canberra, Australia

Place of publication

Berlin, Germany

Start date

2002-12-02

End date

2002-12-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783540001973

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2002, Springer-Verlag Berlin Heidelberg

Editor/Contributor(s)

B McKay, J Slaney

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