Name
Title | ||
|---|---|---|
Random Inspection Evaluation Of Shanghai Graduate Dissertation Based On Bayesian Decision Tree |
Abstract | ||
|---|---|---|
In order to improve the quality of graduate dissertation and examine the quality of graduate education, the mechanism of graduate dissertation random inspection evaluation in Shanghai has been operated for more than ten years. Evaluation experts evaluate the quality of dissertation by using subitem evaluation method rather than comprehensive evaluation method to reduce the risk of misjudgment. Decision tree is a model to explain the data processing from subitem evaluation to comprehensive evaluation. To reduce the disadvantage of decision tree algorithm, the Bayesian decision tree is applied for denoising. The experiment result shows that our method can predict the comprehensive evaluations effectively according to subitem evaluations. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICCSE.2016.7581631 | 2016 11TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE) |
Keywords | Field | DocType |
Graduate dissertation, Subitem evaluation, Comprehensive evaluation, Bayesian decision tree | Decision tree,Data processing,Computer science,Graduate education,Artificial intelligence,Statistical classification,Machine learning,Decision tree learning,Disadvantage,Bayesian probability | Conference |
ISSN | Citations | PageRank |
2471-6146 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Xu | 1 | 0 | 0.34 |
| Yong Jin | 2 | 0 | 0.34 |
| Shardrom Johnson | 3 | 0 | 0.68 |
| Weishan Gao | 4 | 0 | 0.34 |
| Hui Miao | 5 | 0 | 0.34 |
| Qin Wei | 6 | 0 | 0.34 |