Research about pruning hyper-parameter optimization method based on transfer learning in geographic information system

Recently, researchers have found that some automated optimization methods and techniques can speed up the whole search process and can obtain better model hyper-parameter configurations. In this paper, the optimized initial hyper-parameter configuration is obtained through the loss function defined under the definition of meta-features combined with the algorithm of transfer learning. Furthermore, the sequence model-based hyper-parameter optimization model (SMBO model) is transformed by introducing a pruning strategy. By categorizing the hyper-parameters according to their importance, the non-important hyper-parameters are eliminated but important hyper-parameters are strengthened, so the final hyper-parameter optimization configuration is obtained. It is the parameter optimization design based on this secondary optimization that makes it possible to quickly search and select relevant information and carry out data application. The main goal of this paper is to clarify the mathematical principle of secondary optimization and combine it with geographic information system to create the value development of geographic information database. © 2021, Saudi Society for Geosciences.