File(s) not publicly available
Solving regression problems using competitive ensemble models
conference contribution
posted on 2002-01-01, 00:00 authored by Yakov Frayman, Bernard RolfeBernard Rolfe, G WebbThe 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 IntelligenceEvent
Australian Joint Conference on Artificial Intelligence (15th : 2002 : Canberra, A.C.T.)Pagination
511 - 522Publisher
SpringerLocation
Canberra, AustraliaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2002-12-02End date
2002-12-06ISSN
0302-9743eISSN
1611-3349ISBN-13
9783540001973Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2002, Springer-Verlag Berlin HeidelbergEditor/Contributor(s)
B McKay, J SlaneyUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC