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Effects of the correlation model, the trend model, and the number of training points on the accuracy of K riging metamodels
Author(s) -
Acar Erdem
Publication year - 2013
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2012.00646.x
Subject(s) - kriging , computer science , correlation , exponential function , training (meteorology) , approximation error , artificial intelligence , machine learning , algorithm , mathematics , mathematical analysis , physics , geometry , meteorology
This paper explores the effects of the correlation model, the trend model, and the number of training points on the accuracy of K riging metamodels. G aussian correlation models are found to be superior to exponential and linear correlation models. No particular trend model is found to be better than the other models. The number of training points used in constructing the K riging metamodels is observed to change the relative performances of the trend and the correlation functions. The leave‐one‐out cross‐validation error is found to become a better surrogate for the actual error, as the number of training points is increased. Finally, the use of an ensemble of metamodels is discussed and it is found that using an ensemble may improve the accuracy.