TY - JOUR
T1 - Pruning of support vector networks on flood forecasting
AU - Chen, Shien Tsung
AU - Yu, Pao Shan
N1 - Funding Information:
The authors thank the National Science Council of the Republic of China (Taiwan) for partially supporting this research under Contract No. NSC 94-2625-Z-006-001.
Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2007/12/15
Y1 - 2007/12/15
N2 - Support vector machine (SVM), which is analytically solved to reach its optimal structural formula, can be represented as a network architecture resembling artificial neural networks (multilayer perceptrons) that have been pruned to obtain model parsimony or improve generalization. This study presents two methods of pruning the support vector networks, and applies them to a case study of real-time flood stage forecasting. One method prunes the input variables by the cross-correlation method, while the other prunes the support vectors according to the derived relationship among SVM parameters. These pruning methods do not revise the SVM algorithm, and the pruned models therefore still have the optimal architecture. The real-time forecasting performance pertaining to the original and the pruned SVM models are compared, and comparison results indicate that the pruning reduces the network complexity but does not degrade the forecasting ability. Moreover, the deletion of support vectors and the change in their weights during the pruning process are identified, revealing that the support vectors with small weight (and hence less significant) tend to be pruned off, while the support vectors that are more informative to characterize the floods are preserved after the pruning. Finally, this study suggests that the proposed support vector pruning method be a potential data mining technique.
AB - Support vector machine (SVM), which is analytically solved to reach its optimal structural formula, can be represented as a network architecture resembling artificial neural networks (multilayer perceptrons) that have been pruned to obtain model parsimony or improve generalization. This study presents two methods of pruning the support vector networks, and applies them to a case study of real-time flood stage forecasting. One method prunes the input variables by the cross-correlation method, while the other prunes the support vectors according to the derived relationship among SVM parameters. These pruning methods do not revise the SVM algorithm, and the pruned models therefore still have the optimal architecture. The real-time forecasting performance pertaining to the original and the pruned SVM models are compared, and comparison results indicate that the pruning reduces the network complexity but does not degrade the forecasting ability. Moreover, the deletion of support vectors and the change in their weights during the pruning process are identified, revealing that the support vectors with small weight (and hence less significant) tend to be pruned off, while the support vectors that are more informative to characterize the floods are preserved after the pruning. Finally, this study suggests that the proposed support vector pruning method be a potential data mining technique.
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U2 - 10.1016/j.jhydrol.2007.08.029
DO - 10.1016/j.jhydrol.2007.08.029
M3 - Article
AN - SCOPUS:35748941976
SN - 0022-1694
VL - 347
SP - 67
EP - 78
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - 1-2
ER -