Gaussian Process Regression Model Based on Random Sampling and Secondary Encoding Techniques

Abstract

Gaussian Process Regression (GPR) is a flexible non-parametric method that has been widely used in various prediction tasks due to its superior performance in fitting nonlinear functions. However, as the sample size increases, the computational complexity of GPR models grows exponentially, limiting their application to large-scale datasets. To address this issue, this paper proposes a GPR model based on the Stacking framework. The core innovation of the model consists of two parts: first, random sampling techniques are employed to extract multiple subsamples from the original dataset, and independent GPR models are trained for each subsample. Since the subsample sizes are relatively small, this strategy effectively reduces computational complexity and further improves efficiency through parallel processing of multiple models. Second, to overcome the performance variance among different submodels, a model fusion mechanism is adopted. The predictions from the individual submodels are treated as new features, and a secondary GPR model is trained as a combiner to optimize the aggregation of these predictions. This two-layer structural design not only significantly reduces the computational cost of GPR but also enhances the generalization capability of the predictive model through model fusion. Simulation experiments and real-world data analyses demonstrate that the proposed method exhibits a clear competitive advantage over traditional regression models.