A Monte Carlo and Kernel Density Estimation based virtual sample generation method for small data modeling problem

In early industrial production, due to the limited resources, enterprises need to use the limited data to analyze the production status and product quality in order to reduce the waste of resources and funds. This requires building a model with high accuracy. Due to the small amount of data, the accuracy of the model based on small samples is low. The technologY of generating virtual sample is often used, according to the information interval between sample data to fill in it with an effective way to expand the amount of sample data. A novel kernel density estimation based on distribution with sample output variables is proposed. Monte Carlo sampling is used to fill the gap between sample distribution and realize the uniform distribution of samples. Combined with Bagging-RBF neural network and bat algorithm (BA), effective virtual samples are generated. Two experiments, MLCC and PTA, show that the virtual samples arc more effective.