  Machine learning classification techniques have been used widely to recognize
the feasible design domain and discover hidden patterns in engineering design.
An accurate classification model needs a large dataset; however, generating a
large dataset is costly for complex simulation-based problems. After training
by a small dataset, surrogate models can generate a large pseudo dataset
efficiently. Errors, however, may be introduced by surrogate modeling. This
paper investigates the mutual effect of a large pseudo dataset and surrogate
modeling uncertainty. Four widely used methods, i.e., Naive Bayes classifier,
support vector machine, random forest regression, and artificial neural network
for classification, are studied on four benchmark problems. Kriging is used as
the basic surrogate model method. The results show that a large pseudo dataset
improves the classification accuracy, which depends on both design problems and
classification algorithms. Except for the Naive Bayes, the other three methods
are recommended for using pseudo data to improve classification performance.
Also, a wind turbine design problem is used to illustrate the effect of the
pseudo dataset on feasible subspace recognition. The large pseudo dataset
improves the recognized subspace bound greatly, which can be reproduced by
classification models well except for the Naive Bayes classifier. Under the
uncertainty of surrogate modeling, the random forest presents high robustness
to recognize the feasible design domain bound, while the artificial neural
network demonstrates a high sensitivity to uncertainty with the recognized
bound deteriorated.
