Resource Sharing Algorithm of Ideological and Political Course Based on Random Forest

• Published in: Mathematical problems in engineering Vol. 2022; pp. 1 - 8
• Main Author: Lei, Xiaoli
• Format: Journal Article
• Language: English
• Published: New York Hindawi 21.05.2022; John Wiley & Sons, Inc
• Subjects: Classrooms; College campuses; Colleges & universities; Computers; Construction; Cost function; Data transmission; Education; Educational technology; Genetic algorithms; Ideology; Internet resources; Learning; Machine learning; Mathematical analysis; Mathematical models; Network computers; Online instruction; Optimization; Resource allocation; Resource sharing; Science; Students; Teaching
• ISSN: 1024-123X; 1563-5147
• DOI: 10.1155/2022/8765166

Three aspects of the system’s online resource distribution and application are built around subject, object, and intermediary resources. The invention relates to a method for allocating resources based on the random forest algorithm. The resource allocation process entails the following steps: constructing a mathematical model of the resource allocation process, defining a mathematical model of the resource allocation process for the target object, and designing the cost function. The training data set for random forest is constructed using the classification concept. It is based on the mathematical model of resource allocation and cost function. Generation of random forests and prediction of target objects are based on historical data. Resource allocation steps are based on predictive structure. The invention provides a resource allocation method that satisfies task completion degree constraints and includes a resource allocation algorithm based on random forest with a high probability of finding an optimal solution. It also addresses the issue that intelligent optimization algorithms such as genetic algorithms are prone to fall into local optimum.