DocumentCode :
423681
Title :
Semi-optimal hierarchical regression models and ANNs
Author :
Solomatine, Dimitri P. ; Siek, Michael Baskara L A
Author_Institution :
Inst. for Water Educ., UNESCO, Delft, Netherlands
Volume :
2
fYear :
2004
fDate :
25-29 July 2004
Firstpage :
1173
Abstract :
A hierarchical modular model is comprised of a set of specialized models (committee machine) that are constructed in hierarchical (tree) fashion and each of which is responsible for a particular region of input space. Many algorithms in this class, for example M5 model tree, are greedy and hence far from being optimal. An algorithmic framework leading to building more accurate optimal and semi-optimal trees is proposed. Its particular implementation for regression problems, M5opt algorithm, constructs model trees that are more accurate than those obtained by the greedy approach of M5, and in a number of cases more accurate than ANNs.
Keywords :
greedy algorithms; neural nets; regression analysis; trees (mathematics); artificial neural networks; committee machine model; greedy algorithm; hierarchical modular model; model tree construction; regression problems; semioptimal hierarchical regression models; Boosting; Buildings; Classification tree analysis; Decision trees; Input variables; Linear regression; Mars; Neural networks; Regression tree analysis; Software algorithms;
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.2004.1380104
Filename :
1380104
Link To Document :
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