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A paper quality and comment consistency detection model based on feature dimensionality reduction.
- Source :
- Alexandria Engineering Journal; Dec2022, Vol. 61 Issue 12, p10395-10405, 11p
- Publication Year :
- 2022
-
Abstract
- As a reflection of the scholar's mastery of basic theories and professional knowledge, the dissertation is an important yardstick for measuring the level of scientific research. At present, the problem of academic misconduct is becoming increasingly prominent, which is not only related to personal academic ethics, but also related to the overall development of the national academic and scientific research field. In the traditional method of evaluating the quality of papers, it is mainly based on the evaluation experts' comments and scores. However, there are cases that the evaluation experts' comments and scores are inconsistent in practice. To address this problem, we proposed a paper quality consistency detection model based on the nearest neighbor analysis dimensionality reduction algorithm. Compared with other traditional models, the experimental results show that the detection accuracy of XGBoost model after dimensionality reduction using nearest neighbor analysis algorithm reaches 85.81%. [ABSTRACT FROM AUTHOR]
- Subjects :
- DIMENSIONAL reduction algorithms
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Details
- Language :
- English
- ISSN :
- 11100168
- Volume :
- 61
- Issue :
- 12
- Database :
- Supplemental Index
- Journal :
- Alexandria Engineering Journal
- Publication Type :
- Academic Journal
- Accession number :
- 160909827
- Full Text :
- https://doi.org/10.1016/j.aej.2022.03.074