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Ensemble of surrogates based on error classification by unsupervised learning | IEEE Conference Publication | IEEE Xplore

Ensemble of surrogates based on error classification by unsupervised learning


Abstract:

Surrogate modeling is a common method for computationally intensive engineering design optimization problems. For lack of prior knowledge, it is difficult to decide which...Show More

Abstract:

Surrogate modeling is a common method for computationally intensive engineering design optimization problems. For lack of prior knowledge, it is difficult to decide which surrogate is more suitable for approximation. In order to take full advantage of multiple surrogates, ensembles of surrogate models have been gradually focused on. However, the current ensemble methods do not consider the relation between weights and error variation of component surrogates, which is helpful for improving the prediction accuracy. In this paper, a novel point-wise weighted ensemble approach of surrogate models is proposed, which combines error classification and nearest neighbour choosing into point-wise weights computing. First, the leave-one-out cross validation errors of each component surrogate are classified into several error levels based on unsupervised learning. Second, nearest neighbour training points of each test point are selected according to distance sorting. Considering local error of each test point, only nearest neighbour training points which achieve a user-defined error level of each component surrogate are selected. Finally, based on the above steps, the point-wise weights of each test point are computed for the component surrogates. Experiments show that the proposed ensemble method outperforms the state-of-the-art methods in three benchmark function examples.
Date of Conference: 24-29 July 2016
Date Added to IEEE Xplore: 21 November 2016
ISBN Information:
Conference Location: Vancouver, BC, Canada

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