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Dynamic Knowledge Inference Based on Bayesian Network Learning
- Source :
- Mathematical Problems in Engineering, Vol 2020 (2020)
- Publication Year :
- 2020
- Publisher :
- Hindawi Limited, 2020.
-
Abstract
- On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system.
- Subjects :
- Article Subject
Computer science
General Mathematics
0206 medical engineering
Inference
02 engineering and technology
Machine learning
computer.software_genre
0202 electrical engineering, electronic engineering, information engineering
Maximum a posteriori estimation
ComputingMilieux_COMPUTERSANDEDUCATION
QA1-939
Structure (mathematical logic)
Basis (linear algebra)
business.industry
Node (networking)
General Engineering
Process (computing)
Bayesian network
Physics::Physics Education
Probabilistic inference
Engineering (General). Civil engineering (General)
020601 biomedical engineering
020201 artificial intelligence & image processing
Artificial intelligence
TA1-2040
business
computer
Mathematics
Subjects
Details
- Language :
- English
- ISSN :
- 15635147
- Volume :
- 2020
- Database :
- OpenAIRE
- Journal :
- Mathematical Problems in Engineering
- Accession number :
- edsair.doi.dedup.....e75c7af620c9f820aa44f04f06eabffd