Non-parametric Statistical Assistance in Virtual Sample Selection for Small Data Set Prediction

Science learned models based on limited data are usually fragile, researchers suggest the adoption of virtual samples to improve the prediction model. In this study, nonparametric statistical tool, Kolmogorov-Smirnov test, is introduced to examine the distribution of virtual samples without any assumption about the underlying population. The examination procedure would help control the quality of the generated virtual samples, such that the prediction model can be more robust with the basis of high quality virtual samples. Experimental results show that the prediction model with statistical test procedure performs better than the original one, with more stable and improved accuracies, and the examination procedure can effectively lower the prediction error.