Solving regression problems using competitive ensemble models

Frayman, Yakov, Rolfe, Bernard F. and Webb, Geoffrey I. 2002, Solving regression problems using competitive ensemble models, in AI 2002 : Advances in artificial intelligence : proceedings of the 15th Australian Joint Conference on Artificial Intelligence, Springer, Berlin, Germany, pp. 511-522, doi: 10.1007/3-540-36187-1_45.

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Title Solving regression problems using competitive ensemble models
Author(s) Frayman, Yakov
Rolfe, Bernard F.ORCID iD for Rolfe, Bernard F.
Webb, Geoffrey I.
Conference name Australian Joint Conference on Artificial Intelligence (15th : 2002 : Canberra, A.C.T.)
Conference location Canberra, Australia
Conference dates 2-6 December 2002
Title of proceedings AI 2002 : Advances in artificial intelligence : proceedings of the 15th Australian Joint Conference on Artificial Intelligence
Editor(s) McKay, Bob
Slaney, John
Publication date 2002
Start page 511
End page 522
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) computer science
Summary 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.
ISBN 9783540001973
ISSN 0302-9743
Language eng
DOI 10.1007/3-540-36187-1_45
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2002, Springer-Verlag Berlin Heidelberg
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Document type: Conference Paper
Collection: School of Information Technology
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