Academic Journal
Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm.
| Τίτλος: | Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm. |
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| Συγγραφείς: | Wang, Hongyan |
| Πηγή: | Complexity; 3/20/2021, p1-11, 11p |
| Θεματικοί όροι: | Data mining, K-nearest neighbor classification, Nearest neighbor analysis (Statistics), Algorithms, Parallel algorithms, Classification algorithms, Naive Bayes classification |
| Περίληψη: | This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. [ABSTRACT FROM AUTHOR] |
| Copyright of Complexity is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 149393817 RelevancyScore: 900 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 899.884582519531 |
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| Items | – Name: Title Label: Title Group: Ti Data: Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Hongyan%22">Wang, Hongyan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Complexity; 3/20/2021, p1-11, 11p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22K-nearest+neighbor+classification%22">K-nearest neighbor classification</searchLink><br /><searchLink fieldCode="DE" term="%22Nearest+neighbor+analysis+%28Statistics%29%22">Nearest neighbor analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Classification+algorithms%22">Classification algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Naive+Bayes+classification%22">Naive Bayes classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents the concept and algorithm of data mining and focuses on the linear regression algorithm. Based on the multiple linear regression algorithm, many factors affecting CET4 are analyzed. Ideas based on data mining, collecting history data and appropriate to transform, using statistical analysis techniques to the many factors influencing the CET-4 test were analyzed, and we have obtained the CET-4 test result and its influencing factors. It was found that the linear regression relationship between the degrees of fit was relatively high. We further improve the algorithm and establish a partition-weighted K-nearest neighbor algorithm. The K-weighted K nearest neighbor algorithm and the partition algorithm are used in the CET-4 test score classification prediction, and the statistical method is used to study the relevant factors that affect the CET-4 test score, and screen classification is performed to predict when the comparison verification will pass. The weight K of the input feature and the adjacent feature are weighted, although the allocation algorithm of the adjacent classification effect has not been significantly improved, but the stability classification is better than K-nearest neighbor algorithm, its classification efficiency is greatly improved, classification time is greatly reduced, and classification efficiency is increased by 119%. In order to detect potential risk graduating students earlier, this paper proposes an appropriate and timely early warning and preschool K-nearest neighbor algorithm classification model. Taking test scores or make-up exams and re-learning as input features, the classification model can effectively predict ordinary students who have not graduated. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Complexity is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/2021/5577868 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 1 Subjects: – SubjectFull: Data mining Type: general – SubjectFull: K-nearest neighbor classification Type: general – SubjectFull: Nearest neighbor analysis (Statistics) Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Parallel algorithms Type: general – SubjectFull: Classification algorithms Type: general – SubjectFull: Naive Bayes classification Type: general Titles: – TitleFull: Analysis and Prediction of CET4 Scores Based on Data Mining Algorithm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Hongyan IsPartOfRelationships: – BibEntity: Dates: – D: 20 M: 03 Text: 3/20/2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 10762787 Titles: – TitleFull: Complexity Type: main |
| ResultId | 1 |