AdaBoost.RT: a boosting algorithm for regression problems

Author

Solomatine, D.P. ; Shrestha, D.L.

Author_Institution

Inst. for Water Educ., UNESCO, Delft, Netherlands

Volume

2

fYear

2004

fDate

25-29 July 2004

Firstpage

1163

Abstract

A boosting algorithm, AdaBoost.RT, is proposed for regression problems. The idea is to filter out examples with a relative estimation error that is higher than the pre-set threshold value, and then follow the AdaBoost procedure. Thus it requires to select the sub-optimal value of relative error threshold to demarcate predictions from the predictor as correct or incorrect. Some experimental results using the M5 model tree as a weak learning machine for benchmark data sets and for hydrological modeling are reported, and compared to other boosting methods, bagging and artificial neural networks, and to a single M5 model tree. AdaBoost.Rt is proved to perform better on most of the considered data sets.

Keywords

estimation theory; hydrology; learning (artificial intelligence); neural nets; pattern classification; regression analysis; trees (mathematics); AdaBoost.RT; M5 model tree; artificial neural networks; benchmark data sets; boosting algorithm; estimation error filtering; hydrological modeling; preset threshold value; regression problems; relative error threshold; weak learning machine; Accuracy; Artificial neural networks; Bagging; Boosting; Classification algorithms; Error correction; Estimation error; Filtering; Machine learning; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-8359-1

Type

conf

DOI

10.1109/IJCNN.2004.1380102

Filename

1380102