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

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: const...

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Veröffentlicht in:Mathematical problems in engineering Jg. 2022; S. 1 - 8
1. Verfasser: Lei, Xiaoli
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Hindawi 21.05.2022
John Wiley & Sons, Inc
Schlagworte:
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
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Beschreibung
Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1024-123X
1563-5147
DOI:10.1155/2022/8765166