Abstract: Eensemble learning model can effectively solve the problems of single model structure,stability and weak predictive ability.However,due to the complexity of its structure,problems such as low operating efficiency and excessive storage cost often occur.Selective ensemble algorithms are often used to optimize ensemble learning models to solve these problems.The currently proposed selective ensemble algorithm still has the phenomenon of insufficient operating effect and efficiency improvement.In order to make up for these shortcomings,a selective ensemble algorithm based on the stacking ensemble framework is proposed.It mainly uses the agglomerated hierarchical clustering(AHC) algorithm and the metropolis criterion of simulated annealing to select the type and number of base learners.In terms of empirical analysis,domestic and foreign online loan data are used separately to build the model.Experimental results prove that the selective ensemble model of AHC-Metropolis can effectively improve the computational efficiency,predictive ability,stability and generalization ability.It is helpful for regulating the order of the Internet financial industry,assist in financial supervision tasks,and provide an effective basis for establishing our country’s financial risk control management system and guaranteeing national financial security.

Key words: Hierarchical clustering, Simulated annealing, Selective ensemble, Financial risk control