Intelligent decision support systems in information systems: integrated learning algorithms and applications

In this paper, based on the algorithms of traditional integration learning model Bagging, Boosting, Stacking, etc., the latest type of deep integration learning model is proposed, and the two parts of implicit and display deep integration are introduced respectively. The deep integration learning model, artificial intelligence and other technologies are utilized to construct an intelligent decision support system for financial risk. Individual stock information of finance companies is selected as data samples and machine learning model is utilized for comparative study. The results show that the response time of the financial risk intelligent decision support system in this paper is within the range of 10s-13s with good stability under 1~10 different financial risk situations. Compared with the existing system of a finance company, the financial risk incidence rate of this paper’s system is 8.26%-10.23%, which is a lower risk incidence rate. The results obtained by the deep integrated learning algorithm are all within a certain range, which is better compared to other models.