Research on a Risk Control Model Based on Selective Ensemble Algorithm of Hierarchical Clustering and Simulated Annealing

Abstract

In order to solve the problems in the traditional ensemble learning model, selective ensemble algorithms are often used to optimize the ensemble learning model. To settle the shortcomings of the current selective ensemble algorithm existing in the selection efficiency and predict outcomes, this paper proposes selective ensemble algorithm basing on stacking ensemble framework. The algorithm 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, Lending-Club data is used to build a multi-classification model. The experimental results show that compared with the single learner model, the AHC-Metropolis selective ensemble algorithm has better performance and stability in predicting multi-classification problems, and can provide a basis for improving the financial risk control system and ensuring national security.